The base class for all user-defined models. More...

#include <BCModel.h>

Inheritance diagram for BCModel:

Collaboration diagram for BCModel:

Public Member Functions
Constructors and destructors
	BCModel (const std::string &name="model")
	Default constructor. More...

	BCModel (const BCModel &bcmodel)
	Copy constructor. More...

	BCModel (const std::string &filename, const std::string &name, bool loadObservables=true)
	Read in MCMC constructor. More...

BCModel &	operator= (const BCModel &)
	Copy-assignment operator.

virtual	~BCModel ()
	Destructor. More...

Member functions (get)
BCDataSet *	GetDataSet ()

unsigned	GetNDataPoints () const

int	GetNDoF () const

virtual BCPriorModel *	GetPriorModel (bool prepare=true, bool call_likelihood=false)

virtual BCH1D	GetPrior (unsigned index)
	Get prior of a variable as a BCH1D. More...

virtual BCH2D	GetPrior (unsigned index1, unsigned index2)
	Get prior of a pair of variables as a BCH2D. More...

BCH1D &	GetBCH1DPriorDrawingOptions ()

BCH2D &	GetBCH2DPriorDrawingOptions ()

BCH1D &	GetBCH1DPosteriorDrawingOptions ()

BCH2D &	GetBCH2DPosteriorDrawingOptions ()

bool	GetDrawPriorFirst () const

Member functions (set)
void	SetDataSet (BCDataSet *dataset)
	Sets the data set. More...

void	SetKnowledgeUpdateDrawingStyle (BCAux::BCKnowledgeUpdateDrawingStyle style=BCAux::kKnowledgeUpdateDefaultStyle)
	Set default drawing options for knowledge update plots. More...

void	SetDrawPriorFirst (bool b=true)
	Set drawing of prior first (true) or posterior first (false) for knowledge update plots. More...

Member functions (miscellaneous methods)
virtual double	APrioriProbability (const std::vector< double > &parameters)
	Returns the prior probability. More...

virtual double	LogAPrioriProbability (const std::vector< double > &parameters)
	Returns natural logarithm of the prior probability. More...

virtual double	Likelihood (const std::vector< double > &params)
	Returns the likelihood. More...

virtual double	LogLikelihood (const std::vector< double > &params)=0
	Calculates natural logarithm of the likelihood. More...

virtual double	ProbabilityNN (const std::vector< double > &params)
	Returns the likelihood times prior probability given a set of parameter values. More...

virtual double	LogProbabilityNN (const std::vector< double > &parameters)
	Returns the natural logarithm of likelihood times prior probability given a set of parameter values. More...

virtual double	Probability (const std::vector< double > &parameters)
	Returns the a posteriori probability given a set of parameter values. More...

virtual double	LogProbability (const std::vector< double > &parameters)
	Returns natural logarithm of the a posteriori probability given a set of parameter values. More...

virtual double	SamplingFunction (const std::vector< double > &parameters)
	Sampling function used for importance sampling. More...

virtual double	Eval (const std::vector< double > &parameters)
	Overloaded function to evaluate integral. More...

virtual double	LogEval (const std::vector< double > &parameters)
	Overloaded function to evaluate integral. More...

virtual void	InitializeMarkovChainTree (bool replacetree=false, bool replacefile=false)
	Initialize the trees containing the Markov chains and parameter info. More...

double	HessianMatrixElement (unsigned index1, unsigned index2, const std::vector< double > &point)
	Calculates the matrix element of the Hessian matrix. More...

void	PrintShortFitSummary ()
	Prints a short summary of the fit results on the screen. More...

void	PrintHessianMatrix (std::vector< double > parameters)
	Prints matrix elements of the Hessian matrix. More...

virtual unsigned	PrintKnowledgeUpdatePlots (const std::string &filename, unsigned hdiv=1, unsigned vdiv=1, bool call_likelihood=false)
	Print a comparison of the prior knowledge to the posterior knowledge for each parameter. More...

Public Member Functions inherited from BCIntegrate
	BCIntegrate (const std::string &name="model")
	Default constructor.

	BCIntegrate (const std::string &filename, const std::string &name, bool loadObservables=true)
	Read in MCMC constructor. More...

	BCIntegrate (const BCIntegrate &other)
	Copy constructor.

BCIntegrate &	operator= (const BCIntegrate &)
	Copy-assignment operator.

virtual	~BCIntegrate ()
	Destructor.

double	GetIntegral () const

BCIntegrate::BCOptimizationMethod	GetOptimizationMethod () const

BCIntegrate::BCIntegrationMethod	GetIntegrationMethod () const

BCIntegrate::BCMarginalizationMethod	GetMarginalizationMethod () const

BCIntegrate::BCSASchedule	GetSASchedule () const

void	GetRandomVectorInParameterSpace (std::vector< double > &x) const
	Fills a vector of random numbers x[i] between fMin[i] and fMax[i] into a vector. More...

double	GetRandomPoint (std::vector< double > &x)
	Fills a vector of (flat) random numbers in the limits of the parameters and returns the probability at that point. More...

int	GetNIterationsMin () const

int	GetNIterationsMax () const

int	GetNIterationsPrecisionCheck () const

int	GetNIterations () const

double	GetRelativePrecision () const

double	GetAbsolutePrecision () const

BCCubaMethod	GetCubaIntegrationMethod () const

const BCCubaOptions::Vegas &	GetCubaVegasOptions () const

const BCCubaOptions::Suave &	GetCubaSuaveOptions () const

const BCCubaOptions::Divonne &	GetCubaDivonneOptions () const

const BCCubaOptions::Cuhre &	GetCubaCuhreOptions () const

TH1 *	GetSlice (std::vector< unsigned > indices, unsigned &nIterations, double &log_max_val, const std::vector< double > parameters=std::vector< double >(0), int nbins=0, bool normalize=true)
	Returns a one-dimensional slice of the pdf at the point and along a specific direction. More...

TH1 *	GetSlice (const std::string &name, unsigned &nIterations, double &log_max_val, const std::vector< double > parameters=std::vector< double >(0), int nbins=0, bool normalize=true)
	Returns a one-dimensional slice of the pdf at the point and along a specific direction. More...

TH1 *	GetSlice (unsigned index, unsigned &nIterations, double &log_max_val, const std::vector< double > parameters=std::vector< double >(0), int nbins=0, bool normalize=true)
	Returns a one-dimensional slice of the pdf at the point and along a specific direction. More...

TH2 *	GetSlice (const std::string &name1, const std::string &name2, unsigned &nIterations, double &log_max_val, const std::vector< double > parameters=std::vector< double >(0), int nbins=0, bool normalize=true)
	Returns a two-dimensional slice of the pdf at the point and along two specified directions. More...

TH2 *	GetSlice (unsigned index1, unsigned index2, unsigned &nIterations, double &log_max_val, const std::vector< double > parameters=std::vector< double >(0), int nbins=0, bool normalize=true)
	Returns a two-dimensional slice of the pdf at the point and along two specified directions. More...

double	GetError () const

TMinuitMinimizer &	GetMinuit ()

double	GetSAT0 () const
	Returns the Simulated Annealing starting temperature. More...

double	GetSATmin () const
	Returns the Simulated Annealing threshhold temperature. More...

virtual const std::vector< double > &	GetBestFitParameters () const

const std::vector< double > &	GetBestFitParameterErrors () const
	Returns the set of errors on the values of the parameters at the mode.

double	GetLogMaximum () const
	Returns the posterior at the mode. More...

void	SetFlagIgnorePrevOptimization (bool flag)

void	SetOptimizationMethod (BCIntegrate::BCOptimizationMethod method)

void	SetIntegrationMethod (BCIntegrate::BCIntegrationMethod method)

void	SetMarginalizationMethod (BCIntegrate::BCMarginalizationMethod method)

void	SetSASchedule (BCIntegrate::BCSASchedule schedule)

void	SetNIterationsMin (int niterations)

void	SetNIterationsMax (int niterations)

void	SetNIterationsPrecisionCheck (int niterations)

void	SetRelativePrecision (double relprecision)

void	SetAbsolutePrecision (double absprecision)
	Set absolute precision of the numerical integation.

void	SetCubaIntegrationMethod (BCCubaMethod type)
	Set Cuba integration method.

void	SetCubaOptions (const BCCubaOptions::Vegas &options)
	Set options for CUBA's Vegas. More...

void	SetCubaOptions (const BCCubaOptions::Suave &options)
	Set options for CUBA's Suave. More...

void	SetCubaOptions (const BCCubaOptions::Divonne &options)
	Set options for CUBA's Divonne. More...

void	SetCubaOptions (const BCCubaOptions::Cuhre &options)
	Set options for CUBA's Cuhre. More...

void	SetSAT0 (double T0)
	Set starting temperature for Simulated Annealing. More...

void	SetSATmin (double Tmin)
	Set threshold temperature for Simulated Annealing. More...

double	Normalize ()
	Performs integration. More...

double	Integrate (BCIntegrationMethod intmethod)
	Does the integration over the un-normalized probability. More...

double	Integrate ()
	Perform the integration. More...

double	Integrate (BCIntegrationMethod type, tRandomizer randomizer, tEvaluator evaluator, tIntegralUpdater updater, std::vector< double > &sums)
	Does the integration over the un-normalized probability. More...

double	EvaluatorMC (std::vector< double > &sums, const std::vector< double > &point, bool &accepted)
	Evaluates integrator.

int	MarginalizeAll ()
	Marginalize all probabilities wrt. More...

int	MarginalizeAll (BCMarginalizationMethod margmethod)
	Marginalize all probabilities wrt. More...

virtual void	MarginalizePreprocess ()
	Method executed for before marginalization. More...

virtual void	MarginalizePostprocess ()
	Method executed after marginalization. More...

std::vector< double >	FindMode (std::vector< double > start=std::vector< double >())
	Do the mode finding using a method set via SetOptimizationMethod. More...

std::vector< double >	FindMode (BCIntegrate::BCOptimizationMethod optmethod, std::vector< double > start=std::vector< double >())
	Find mode using a specific method. More...

double	SATemperature (double t) const
	Temperature annealing schedule for use with Simulated Annealing. More...

double	SATemperatureBoltzmann (double t) const
	Temperature annealing schedule for use with Simulated Annealing. More...

double	SATemperatureCauchy (double t) const
	Temperature annealing schedule for use with Simulated Annealing. More...

virtual double	SATemperatureCustom (double t) const
	Temperature annealing schedule for use with Simulated Annealing. More...

std::vector< double >	GetProposalPointSA (const std::vector< double > &x, int t) const
	Generates a new state in a neighbourhood around x that is to be accepted or rejected by the Simulated Annealing algorithm. More...

std::vector< double >	GetProposalPointSABoltzmann (const std::vector< double > &x, int t) const
	Generates a new state in a neighbourhood around x that is to be accepted or rejected by the Simulated Annealing algorithm. More...

std::vector< double >	GetProposalPointSACauchy (const std::vector< double > &x, int t) const
	Generates a new state in a neighbourhood around x that is to be accepted or rejected by the Simulated Annealing algorithm. More...

virtual std::vector< double >	GetProposalPointSACustom (const std::vector< double > &x, int t) const
	Generates a new state in a neighbourhood around x that is to be accepted or rejected by the Simulated Annealing algorithm. More...

std::vector< double >	SAHelperGetRandomPointOnHypersphere () const
	Generates a uniform distributed random point on the surface of a fNvar-dimensional Hypersphere. More...

double	SAHelperGetRadialCauchy () const
	Generates the radial part of a n-dimensional Cauchy distribution. More...

double	SAHelperSinusToNIntegral (int dim, double theta) const
	Returns the Integral of sin^dim from 0 to theta. More...

virtual void	ResetResults ()
	Reset all information on the best-fit parameters. More...

std::string	DumpIntegrationMethod (BCIntegrationMethod type) const
	Return string with the name for a given integration type. More...

std::string	DumpCurrentIntegrationMethod () const
	Return string with the name for the currently set integration type. More...

std::string	DumpUsedIntegrationMethod () const
	Return string with the name for the previously used integration type. More...

std::string	DumpMarginalizationMethod (BCMarginalizationMethod type) const
	Return string with the name for a given marginalization type. More...

std::string	DumpCurrentMarginalizationMethod () const
	Return string with the name for the currently set marginalization type. More...

std::string	DumpUsedMarginalizationMethod () const
	Return string with the name for the marginalization type used. More...

std::string	DumpOptimizationMethod (BCOptimizationMethod type) const
	Return string with the name for a given optimization type. More...

std::string	DumpCurrentOptimizationMethod () const
	Return string with the name for the currently set optimization type. More...

std::string	DumpUsedOptimizationMethod () const
	Return string with the name for the optimization type used to find the current mode. More...

std::string	DumpCubaIntegrationMethod (BCCubaMethod type) const
	Return string with the name for a given Cuba integration type. More...

std::string	DumpCubaIntegrationMethod () const
	Return string with the name for the currently set Cuba integration type. More...

void	SetBestFitParameters (const std::vector< double > &x)
	Set best fit parameters values. More...

void	SetBestFitParameters (const std::vector< double > &x, const double &new_value, double &old_value)

bool	CheckMarginalizationAvailability (BCMarginalizationMethod type)
	Check availability of integration routine for marginalization. More...

bool	CheckMarginalizationIndices (TH1 *hist, const std::vector< unsigned > &index)
	Check that indices of parameters to marginalize w/r/t are correct.

double	IntegrateLaplace ()
	Integrate using the Laplace approximation. More...

Public Member Functions inherited from BCEngineMCMC
	BCEngineMCMC (const std::string &name="model")
	Default constructor. More...

	BCEngineMCMC (const BCEngineMCMC &enginemcmc)
	Copy constructor. More...

	BCEngineMCMC (const std::string &filename, const std::string &name, bool loadObservables=true)
	Read in MCMC constructor. More...

BCEngineMCMC &	operator= (const BCEngineMCMC &)
	Copy-assignment operator.

virtual	~BCEngineMCMC ()
	Destructor. More...

const std::string &	GetName () const

const std::string &	GetSafeName () const

unsigned	GetNChains () const

unsigned	GetNLag () const

int	GetCurrentIteration () const

unsigned	GetCurrentChain () const

int	GetNIterationsConvergenceGlobal () const

unsigned	GetNIterationsPreRun () const

unsigned	GetNIterationsPreRunMin () const

unsigned	GetNIterationsPreRunMax () const

unsigned	GetNIterationsRun () const

unsigned	GetNIterationsPreRunCheck () const

unsigned	GetPreRunCheckClear ()

double	GetMinimumEfficiency () const

double	GetMaximumEfficiency () const

double	GetScaleFactorLowerLimit () const

double	GetScaleFactorUpperLimit () const

const std::vector< std::vector< double > > &	GetScaleFactors () const

const ChainState &	GetChainState (unsigned c) const

const std::vector< double > &	Getx (unsigned c) const

double	Getx (unsigned c, unsigned p) const

double	GetLogProbx (unsigned c) const

BCEngineMCMC::Phase	GetPhase () const

BCEngineMCMC::InitialPositionScheme	GetInitialPositionScheme () const

unsigned	GetInitialPositionAttemptLimit () const

bool	GetProposeMultivariate () const

double	GetProposalFunctionDof () const

unsigned	GetMultivariateCovarianceUpdates () const

double	GetMultivariateCovarianceUpdateLambda () const

double	GetMultivariateEpsilon () const

double	GetMultivariateScaleMultiplier () const

double	GetRValueParametersCriterion () const

const std::vector< double > &	GetRValueParameters () const

double	GetRValueParameters (unsigned index) const

bool	GetCorrectRValueForSamplingVariability () const
	Flag for correcting convergence checking for initial sampling variability. More...

bool	GetFlagRun () const

TTree *	GetMarkovChainTree () const
	Retrieve the tree containing the Markov chain. More...

TTree *	GetParameterTree () const
	Retrieve the tree containing the parameter information. More...

TFile *	GetOutputFile () const
	Retrieve output file for MCMC. More...

const BCEngineMCMC::Statistics &	GetStatistics () const
	Get combined statistics for all chains. More...

const BCEngineMCMC::Statistics &	GetStatistics (unsigned c) const
	Get MCMC statistics for one chain. More...

const std::vector< BCEngineMCMC::Statistics > &	GetStatisticsVector () const
	Get vector of MCMC statistics for each chain separately. More...

bool	GetRescaleHistogramRangesAfterPreRun () const

double	GetHistogramRescalePadding () const

virtual std::vector< unsigned >	GetH1DPrintOrder () const

virtual std::vector< std::pair< unsigned, unsigned > >	GetH2DPrintOrder () const

bool	MarginalizedHistogramExists (unsigned index) const

bool	MarginalizedHistogramExists (unsigned index1, unsigned index2) const

TH1 *	GetMarginalizedHistogram (const std::string &name) const
	Obtain the individual marginalized distributions with respect to one parameter as a ROOT TH1. More...

TH1 *	GetMarginalizedHistogram (unsigned index) const
	Obtain the individual marginalized distributions with respect to one parameter as a ROOT TH1. More...

TH2 *	GetMarginalizedHistogram (const std::string &name1, const std::string &name2) const
	Obtain the individual marginalized distributions with respect to two parameters as a ROOT TH2. More...

TH2 *	GetMarginalizedHistogram (unsigned index1, unsigned index2) const
	Obtain the individual marginalized distributions with respect to two parameters as a ROOT TH2. More...

BCH1D	GetMarginalized (const std::string &name) const
	Obtain the individual marginalized distributions with respect to one parameter. More...

BCH1D	GetMarginalized (unsigned index) const
	Obtain the individual marginalized distributions with respect to one parameter. More...

BCH2D	GetMarginalized (const std::string &name1, const std::string &name2) const
	Obtain the individual marginalized distributions with respect to two parameters. More...

BCH2D	GetMarginalized (unsigned index1, unsigned index2) const
	Obtain the individual marginalized distributions with respect to two parameters. More...

unsigned	GetMaximumParameterNameLength (bool observables=true) const

BCVariable &	GetVariable (unsigned index)

const BCVariable &	GetVariable (unsigned index) const

unsigned	GetNVariables () const

BCParameterSet &	GetParameters ()

const BCParameterSet &	GetParameters () const

BCParameter &	GetParameter (unsigned index)

const BCParameter &	GetParameter (unsigned index) const

BCParameter &	GetParameter (const std::string &name)

const BCParameter &	GetParameter (const std::string &name) const

unsigned	GetNParameters () const

unsigned	GetNFixedParameters () const

unsigned	GetNFreeParameters () const

BCObservableSet &	GetObservables ()

const BCObservableSet &	GetObservables () const

BCObservable &	GetObservable (unsigned index)

const BCObservable &	GetObservable (unsigned index) const

BCObservable &	GetObservable (const std::string &name)

const BCObservable &	GetObservable (const std::string &name) const

unsigned	GetNObservables () const

const std::vector< double > &	GetLocalModes (bool force_recalculation=false)

bool	GetReuseObservables () const

BCH1D &	GetBCH1DdrawingOptions ()

BCH2D &	GetBCH2DdrawingOptions ()

void	SetName (const std::string &name)
	Sets the name of the engine. More...

void	SetScaleFactorLowerLimit (double l)
	Set scale factor lower limit.

void	SetScaleFactorUpperLimit (double l)
	Set scale factor upper limit.

void	SetInitialScaleFactors (const std::vector< double > &scale)
	Set the initial scale factors for the factorized proposal function. More...

void	SetNChains (unsigned n)
	Sets the number of Markov chains which are run in parallel. More...

void	SetNLag (unsigned n)
	Sets the lag of the Markov chains.

void	SetNIterationsPreRunMax (unsigned n)
	Sets the maximum number of iterations in the pre-run. More...

void	SetNIterationsRun (unsigned n)
	Sets the number of iterations. More...

void	SetNIterationsPreRunMin (unsigned n)
	Sets the minimum number of iterations in the pre-run.

void	SetNIterationsPreRunCheck (unsigned n)
	Sets the number of iterations between scale adjustments and convergence checks in the pre-run. More...

void	SetPreRunCheckClear (unsigned n)
	Sets the number of prerun checks to make inbetween statistics clearing. More...

void	SetMinimumEfficiency (double efficiency)
	Sets the minimum efficiency required for a chain. More...

void	SetMaximumEfficiency (double efficiency)
	Sets the maximum efficiency required for a chain. More...

void	SetRandomSeed (unsigned seed)
	Set the random number seed.

void	SetInitialPositions (const std::vector< double > &x0s)
	Sets the initial positions for all chains. More...

void	SetInitialPositions (const std::vector< std::vector< double > > &x0s)
	Sets the initial positions for all chains. More...

void	SetInitialPositionScheme (BCEngineMCMC::InitialPositionScheme scheme)
	Sets flag which defines initial position. More...

void	SetInitialPositionAttemptLimit (unsigned n)
	Sets maximum number of attempts to find a valid initial position. More...

void	SetProposeMultivariate (bool flag)
	Set `flag` to `true` to turn on the multivariate proposal for MCMC based on (Haario et al., 2001) where the covariance is learned from the prerun. More...

void	SetProposalFunctionDof (double dof=1)
	Set the degree of freedom of the proposal function for MCMC. More...

void	SetMultivariateCovarianceUpdateLambda (double l)
	Set weighting for multivariate proposal function covariance update. More...

void	SetMultivariateEpsilon (double epsilon)
	Sets multivariate-proposal-function cholesky-decomposition nudge. More...

void	SetMultivariateScaleMultiplier (double s)
	Sets multivariate-proposal-function scale multiplier. More...

void	SetFlagFillHistograms (bool flag)
	Sets whether to fill histograms. More...

void	SetFlagFillHistograms (bool flag_1d, bool flag_2d)
	Sets the whether to fill histograms. More...

void	SetFillHistogramParPar (unsigned x, unsigned y, bool flag=true)
	Sets whether to fill particular H2 histogram: par(y) vs. More...

void	SetFillHistogramParPar (const std::string &x, const std::string &y, bool flag=true)
	Sets whether to fill particular H2 histogram: par(y) vs. More...

void	SetFillHistogramParObs (unsigned x, unsigned y, bool flag=true)
	Sets whether to fill particular H2 histogram: obs(y) vs. More...

void	SetFillHistogramParObs (const std::string &x, const std::string &y, bool flag=true)
	Sets whether to fill particular H2 histogram: obs(y) vs. More...

void	SetFillHistogramObsObs (unsigned x, unsigned y, bool flag=true)
	Sets whether to fill particular H2 histogram: obs(y) vs. More...

void	SetFillHistogramObsObs (const std::string &x, const std::string &y, bool flag=true)
	Sets whether to fill particular H2 histogram: obs(y) vs. More...

void	SetFillHistogramObsPar (unsigned x, unsigned y, bool flag=true)
	Sets whether to fill particular H2 histogram: par(y) vs. More...

void	SetFillHistogramObsPar (const std::string &x, const std::string &y, bool flag=true)
	Sets whether to fill particular H2 histogram: par(y) vs. More...

void	SetFlagPreRun (bool flag)
	Set if a (new) prerun should be performed. More...

void	SetRValueParametersCriterion (double r)
	Sets the parameter R-value criterion for convergence of all chains.

void	SetCorrectRValueForSamplingVariability (bool flag=true)
	Set flag to correct convergence checking for initial sampling variability. More...

void	SetPrecision (BCEngineMCMC::Precision precision)
	Set the precision for the MCMC run. More...

void	SetPrecision (const BCEngineMCMC *other)
	Copy precision for the MCMC run from other model. More...

void	SetPrecision (const BCEngineMCMC &other)
	Copy precision for the MCMC run from other model. More...

void	SetNbins (unsigned int nbins)
	Set the number of bins for the marginalized distribution of all parameters. More...

void	SetReuseObservables (bool flag)

void	SetRescaleHistogramRangesAfterPreRun (bool flag=true)
	Set flag for rescaling histogram ranges after pre-run. More...

void	SetHistogramRescalingPadding (double factor)
	Set enlargement factor of range for when rescaling. More...

void	WriteMarkovChain (bool flag)
	Turn on/off writing of Markov chain to root file. More...

void	WriteMarkovChainRun (bool flag)
	Turn on/off writing of Markov chain to root file during run. More...

void	WriteMarkovChainPreRun (bool flag)
	Turn on/off writing of Markov chain to root file during prerun. More...

void	WriteMarkovChain (const std::string &filename, const std::string &option, bool flag_run=true, bool flag_prerun=true)
	Turn on writing of Markov chain to root file. More...

void	SetPriorConstant (unsigned index)

void	SetPriorConstant (const std::string &name)

void	SetPrior (unsigned index, TF1 &f, bool logL=true)

void	SetPrior (const std::string &name, TF1 &f, bool logL=true)

void	SetPriorDelta (unsigned index, double value)

void	SetPriorDelta (const std::string &name, double value)

void	SetPriorGauss (unsigned index, double mean, double sigma)

void	SetPriorGauss (const std::string &name, double mean, double sigma)

void	SetPriorGauss (unsigned index, double mode, double sigma_below, double sigma_above)

void	SetPriorGauss (const std::string &name, double mode, double sigma_below, double sigma_above)

void	SetPrior (unsigned index, TH1 &h, bool interpolate=false)

void	SetPrior (const std::string &name, TH1 &h, bool interpolate=false)

void	SetPriorConstantAll ()

void	WriteMarginalizedDistributions (const std::string &filename, const std::string &option, bool closeExistingFile=false)
	Write marginalization histograms to file. More...

virtual void	PrintSummary () const
	Prints a summary to the logs. More...

void	PrintParameters (const std::vector< double > &P, void(*output)(const std::string &)=BCLog::OutSummary) const
	Print parameters. More...

unsigned	PrintAllMarginalized (const std::string &filename, unsigned hdiv=1, unsigned vdiv=1) const
	Print all marginalizations. More...

unsigned	PrintParameterPlot (const std::string &filename, int npar=10, double interval_content=68e-2, std::vector< double > quantile_values=std::vector< double >(0), bool rescale_ranges=true) const
	Print a summary plot for the parameters and user-defined observables. More...

bool	DrawParameterPlot (unsigned i0, unsigned npar=0, double interval_content=68e-2, std::vector< double > quantile_values=std::vector< double >(0), bool rescale_ranges=true) const
	Draw a summary plot for the parameters in the range provided to current pad. More...

bool	PrintCorrelationMatrix (const std::string &filename="matrix.pdf") const
	Print a correlation matrix for the parameters. More...

bool	PrintCorrelationPlot (const std::string &filename="correlation.pdf", bool include_observables=true) const
	Print a correlation plot for the parameters. More...

bool	PrintParameterLatex (const std::string &filename) const
	Print a LaTeX table of the parameters. More...

virtual void	CreateHistograms (bool rescale_ranges=false)
	Create histograms from parameter and observable sets. More...

virtual bool	AddParameter (const std::string &name, double min, double max, const std::string &latexname="", const std::string &unitstring="")

virtual bool	AddParameter (BCParameter &parameter)

virtual bool	AddObservable (const std::string &name, double min, double max, const std::string &latexname="", const std::string &unitstring="")

virtual bool	AddObservable (BCObservable &obs)

virtual void	EvaluateObservables ()
	Evaluates user-defined observables at current state of all chains and stores results in fMCMCState.

virtual void	EvaluateObservables (unsigned chain)
	Evaluates user-defined observables at current state of chain and stores results in fMCMCState. More...

virtual void	CalculateObservables (const std::vector< double > &pars)
	Evaluates user-defined observables. More...

virtual double	ProposalFunction (unsigned ichain, unsigned ipar)
	The default proposal function is a Breit-Wigner random walk. More...

bool	GetProposalPointMetropolis (unsigned chain, std::vector< double > &x)
	Return a proposal point for the Metropolis algorithm. More...

bool	GetProposalPointMetropolis (unsigned chain, unsigned parameter, std::vector< double > &x)
	Return a proposal point for the Metropolis algorithm. More...

bool	GetNewPointMetropolis ()
	Generate a new point using the Metropolis algorithm for all chains. More...

bool	GetNewPointMetropolis (unsigned chain)
	Generate a new point using the Metropolis algorithm for one chain. More...

bool	GetNewPointMetropolis (unsigned chain, unsigned parameter)
	Generate a new point using the Metropolis algorithm for one chain, varying only one parameter's value. More...

bool	AcceptOrRejectPoint (unsigned chain, unsigned parameter)
	Accept or rejects a point for a chain and updates efficiency. More...

void	InChainFillHistograms (const ChainState &cs)
	Fill marginalized distributions from a chain state.

void	InChainFillHistograms ()
	Fill marginalized distributions from all chain states.

void	InChainFillTree (const ChainState &cs, unsigned chain_number)
	Write a chain state to the tree.

void	InChainFillTree ()
	Write all chain states to the tree.

bool	Metropolis ()
	Runs Metropolis algorithm. More...

bool	MetropolisPreRun ()
	Runs a pre run for the Metropolis algorithm. More...

void	MCMCInitialize ()
	Resets all containers used in MCMC and initializes starting points. More...

virtual void	MCMCUserInitialize ()
	User hook called from MCMCInitialize(). More...

virtual void	MCMCUserIterationInterface ()
	Interface allowing to execute arbitrary code for each iteration of the MCMC while running the chains after applying the lag but before filling histograms or the output tree. More...

virtual void	MCMCCurrentPointInterface (const std::vector< double > &point, int ichain, bool accepted)
	Interface allowing to execute arbitrary code for each new point of the MCMC whether it is accepted or not. More...

void	LoadParametersFromTree (TTree *partree, bool loadObservables=true)
	Load parameters and observables from tree. More...

void	LoadMCMCParameters (TTree &partree)
	Load MCMC parameters from parameter tree: nchains, proposal function type, scales. More...

virtual bool	ParameterTreeMatchesModel (TTree *partree, bool checkObservables=true)
	Check parameter tree against model. More...

void	LoadMCMC (const std::string &filename, std::string mcmcTreeName="", std::string parameterTreeName="", bool loadObservables=true)
	Load previous MCMC run. More...

void	LoadMCMC (TTree mcmcTree, TTree parTree, bool loadObservables=true)
	Load previous MCMC run. More...

bool	ValidMCMCTree (TTree *tree, bool checkObservables=true) const
	Check tree structure for MCMC tree. More...

bool	ValidParameterTree (TTree *tree) const
	Check tree structure for parameter tree. More...

void	CloseOutputFile ()
	Close the root output file. More...

virtual void	Remarginalize (bool autorange=true)
	Marginalize from TTree. More...

void	PrepareToContinueMarginalization (const std::string &filename, const std::string &mcmcTreeName="", const std::string &parameterTreeName="", bool loadObservables=true, bool autorange=true)
	Continue the marginalization already stored in another file. More...

virtual bool	UpdateMultivariateProposalFunctionCovariances (double a)
	Update multivariate proposal function covariances. More...

virtual bool	UpdateMultivariateProposalFunctionCovariances ()
	Update multivariate proposal function covariances. More...

void	CalculateCholeskyDecompositions ()
	Calculate Cholesky decompositions needed for multivariate proposal function. More...

void	UpdateChainIndex (int chain)
	Keep track of which chain is currently computed (within a thread). More...

Protected Attributes
BCH1D	fBCH1DPosteriorDrawingOptions
	knowledge update plot 1D posterior options. More...

BCH1D	fBCH1DPriorDrawingOptions
	knowledge update plot 1D prior options. More...

BCH2D	fBCH2DPosteriorDrawingOptions
	knowledge update plot 2D posterior options. More...

BCH2D	fBCH2DPriorDrawingOptions
	knowledge update plot 2D prior options. More...

BCDataSet *	fDataSet
	A data set. More...

bool	fDrawPriorFirst
	flag for ordering of drawing of prior and posterior in knowledge update plots. More...

bool	fFactorizedPrior
	flag for whether factorized prior has been used. More...

BCPriorModel *	fPriorModel
	BCPriorModel object for drawing of knowledge update, and saving of samples according to prior. More...

Protected Attributes inherited from BCIntegrate
bool	fFlagIgnorePrevOptimization
	Flag for ignoring older results of optimization.

bool	fFlagMarginalized
	flag indicating if the model was marginalized

double	fSALogProb
	Log probability of current simulated annealing iteration. More...

int	fSANIterations
	Number of iterations for simualted annealing. More...

double	fSAT0
	Starting temperature for Simulated Annealing.

double	fSATemperature
	Current temperature of simulated annealing algorithm. More...

double	fSATmin
	Minimal/Threshold temperature for Simulated Annealing.

std::vector< double >	fSAx
	Current simulated annealing parameter point. More...

Protected Attributes inherited from BCEngineMCMC
BCH1D	fBCH1DdrawingOptions
	A BCH1D (with no histogram) for storing BCH1D drawing options. More...

BCH2D	fBCH2DdrawingOptions
	A BCH2D (with no histogram) for storing BCH2D drawing options. More...

bool	fCorrectRValueForSamplingVariability
	flag for correcting R value for initial sampling variability. More...

std::vector< TH1 * >	fH1Marginalized
	Vector of 1D marginalized distributions.

std::vector< std::vector< TH2 * > >	fH2Marginalized
	Vector of 2D marginalized distributions. More...

double	fHistogramRescalePadding
	factor for enlarging range of histograms when rescaling. More...

unsigned	fInitialPositionAttemptLimit
	Maximum number of attempts to make to set the initial position. More...

BCEngineMCMC::InitialPositionScheme	fInitialPositionScheme
	Variable which defines the initial position. More...

std::vector< double >	fLocalModes
	Vector of local modes. More...

int	fMCMCCurrentIteration
	The current iteration number. More...

double	fMCMCEfficiencyMax
	The maximum allowed efficiency for MCMC.

double	fMCMCEfficiencyMin
	The minimum required efficiency for MCMC.

bool	fMCMCFlagWriteChainToFile
	Flag to write Markov chains to file.

bool	fMCMCFlagWritePreRunToFile
	Flag to write pre run to file.

std::vector< std::vector< double > >	fMCMCInitialPosition
	The intial position of each Markov chain. More...

std::vector< double >	fMCMCInitialScaleFactors
	User-provided initial values of the scale factors of the factorized proposal function. More...

unsigned	fMCMCNChains
	Number of Markov chains ran in parallel.

int	fMCMCNIterationsConvergenceGlobal
	Number of iterations needed for all chains to convergence simultaneously.

unsigned	fMCMCNIterationsPreRunCheck
	Number of iterations between scale adjustments and convergence checks in pre-run. More...

unsigned	fMCMCNIterationsPreRunMax
	Maximum number of iterations for a Markov chain prerun.

unsigned	fMCMCNIterationsPreRunMin
	Minimum number of iterations for the pre-run.

unsigned	fMCMCNIterationsRun
	Number of iterations for a Markov chain run.

unsigned	fMCMCNLag
	The lag for the Markov Chain.

TFile *	fMCMCOutputFile
	Output file for for writing MCMC Tree. More...

std::string	fMCMCOutputFilename
	Output filename for for writing MCMC Tree. More...

std::string	fMCMCOutputFileOption
	Output file open option for for writing MCMC Tree. More...

BCEngineMCMC::Phase	fMCMCPhase
	The phase of the run. More...

unsigned	fMCMCPreRunCheckClear
	Number of iterations between clearing of convergence stats in pre-run. More...

double	fMCMCProposalFunctionDof
	Degree of freedom of Student's t proposal. More...

std::vector< std::vector< double > >	fMCMCProposalFunctionScaleFactor
	Scale factors for proposal functions. More...

bool	fMCMCProposeMultivariate
	Flag for using multivariate proposal function. More...

std::vector< double >	fMCMCRValueParameters
	The R-values for each parameter.

double	fMCMCRValueParametersCriterion
	The R-value criterion for convergence of parameters.

double	fMCMCScaleFactorLowerLimit
	Lower limit for scale factors.

double	fMCMCScaleFactorUpperLimit
	Upper limit for scale factors.

std::vector< ChainState >	fMCMCStates
	The current states of each Markov chain. More...

std::vector< BCEngineMCMC::Statistics >	fMCMCStatistics
	Statistics for each Markov chain. More...

BCEngineMCMC::Statistics	fMCMCStatistics_AllChains
	Statistics across all Markov chains. More...

TTree *	fMCMCTree
	The tree containing the Markov chains. More...

unsigned int	fMCMCTree_Chain
	Chain number for storing into tree.

ChainState	fMCMCTree_State
	MC state object for storing into tree.

bool	fMCMCTreeLoaded
	flag for whether MCMC Tree successfully loaded. More...

bool	fMCMCTreeReuseObservables
	flag for whether to reuse MCMC Tree's observables. More...

double	fMultivariateCovarianceUpdateLambda
	weighting parameter for multivariate-proposal-function covariance update. More...

unsigned	fMultivariateCovarianceUpdates
	Number of multivariate-proposal-function covariance updates performed. More...

double	fMultivariateEpsilon
	multivariate-proposal-function cholesky-decomposition nudge. More...

std::vector< TMatrixD >	fMultivariateProposalFunctionCholeskyDecomposition
	Cholesky decompositions for multivariate proposal function. More...

std::vector< TMatrixDSym >	fMultivariateProposalFunctionCovariance
	Covariance matrices used in multivariate proposal functions. More...

double	fMultivariateScaleMultiplier
	factor to multiply or divide scale factors by in adjusting multivariate-proposal-function scales. More...

std::string	fName
	Name of the engine. More...

BCAux::BCTrash< TObject >	fObjectTrash
	Storage for plot objects with proper clean-up.

BCObservableSet	fObservables
	User-calculated Observables Set.

BCParameterSet	fParameters
	Parameter settings.

TTree *	fParameterTree
	The tree containing the parameter information. More...

TRandom3	fRandom
	Random number generator.

std::vector< std::pair< int, int > >	fRequestedH2
	Vector of pairs of indices for which 2D histograms should be stored. More...

bool	fRescaleHistogramRangesAfterPreRun
	flag for rescaling of histograms after pre-run. More...

std::string	fSafeName
	Safe name of the engine for use in naming ROOT objects. More...

Friends
class	BCPriorModel

Additional Inherited Members
Public Types inherited from BCIntegrate
enum	BCOptimizationMethod { kOptEmpty, kOptSimAnn, kOptMetropolis, kOptMinuit, kOptDefault, NOptMethods }
	An enumerator for the mode finding algorithm. More...

enum	BCIntegrationMethod { kIntEmpty, kIntMonteCarlo, kIntCuba, kIntGrid, kIntLaplace, kIntDefault, NIntMethods }
	An enumerator for integration algorithms. More...

enum	BCMarginalizationMethod { kMargEmpty, kMargMetropolis, kMargMonteCarlo, kMargGrid, kMargDefault, NMargMethods }
	An enumerator for marginalization algorithms. More...

enum	BCSASchedule { kSACauchy, kSABoltzmann, kSACustom, NSAMethods }
	An enumerator for the Simulated Annealing schedule. More...

enum	BCCubaMethod { kCubaVegas, kCubaSuave, kCubaDivonne, kCubaCuhre, kCubaDefault, NCubaMethods }
	An enumerator for Cuba integration methods. More...

typedef void(BCIntegrate::*	tRandomizer) (std::vector< double > &) const
	A pointer for a function that chooses a next random point.

typedef double(BCIntegrate::*	tEvaluator) (std::vector< double > &, const std::vector< double > &, bool &)
	A pointer for a function that evaluates at a point.

typedef void(*	tIntegralUpdater) (const std::vector< double > &, const int &, double &, double &)
	A pointer for a function that updates the integral and absolute precision.

Public Types inherited from BCEngineMCMC
enum	Precision { kLow, kQuick, kMedium, kHigh, kVeryHigh }
	An enumerator for the status of a test. More...

enum	Phase { kPreRun = -1, kUnsetPhase = 0, kMainRun = +1 }
	An enumerator for the phase of the Markov chain. More...

enum	InitialPositionScheme { kInitCenter = 0, kInitRandomUniform = 1, kInitUserDefined = 2, kInitRandomPrior = 3 }
	An enumerator for markov-chain position initialization. More...

Static Public Member Functions inherited from BCIntegrate
static void	IntegralUpdaterMC (const std::vector< double > &sums, const int &nIterations, double &integral, double &absprecision)
	Updates info about integrator.

Static Public Member Functions inherited from BCEngineMCMC
static double	RValue (const std::vector< double > &means, const std::vector< double > &variances, unsigned n, bool correctForSamplingVariability=true)
	Calculate R value of set of batches of samples—represented by their means and variances, all batches containing the same number of samples—according to Brooks & Gelman, "General Methods for Monitoring Convergence of Iterative Simulations," (1988) More...

Protected Member Functions inherited from BCIntegrate
virtual std::string	GetBestFitSummary (unsigned i) const
	Get string summarizing best fit for single variable. More...

unsigned	IntegrationOutputFrequency () const
	Determine frequency of output during integration.

void	LogOutputAtEndOfIntegration (double integral, double absprecision, double relprecision, int nIterations)
	Helper method to output at end of integration. More...

void	LogOutputAtIntegrationStatusUpdate (BCIntegrationMethod type, double integral, double absprecision, int nIterations)
	Helper method to output integration status. More...

void	LogOutputAtStartOfIntegration (BCIntegrationMethod type, BCCubaMethod cubatype)
	Helper method to output at beginning of integration. More...

virtual void	PrintBestFitSummary () const
	Print best fit to log.

virtual void	PrintMarginalizationSummary () const
	Print marginalization to log. More...

Protected Member Functions inherited from BCEngineMCMC
virtual void	PrintModelSummary () const
	Print model summary to log. More...

void	SetFillHistogram (int x, int y, bool flag)
	Set whether to fill 2D histogram y vs x: positive indices for parameters; negative for observables, starting at -1 and going more negative—observable index = -(index+1). More...

unsigned	UpdateFrequency (unsigned N) const
	return appropriate update interval More...

void	UpdateParameterTree ()
	Update Paramater TTree with scales and efficiencies. More...

Detailed Description

The base class for all user-defined models.

Author: Daniel Kollar; Kevin Kröninger; Daniel Greenwald

Version: 1.0

Date: 08.2008

This class represents a model. It contains a container of prior distributions and the likelihood. The methods that implement the prior and the likelihood have to be overloaded by the user in the user defined model class derived from this class.

Definition at line 39 of file BCModel.h.

Constructor & Destructor Documentation

BCModel::BCModel ( const std::string & name = "model" )

Default constructor.

Parameters

name	The name of the model

Definition at line 29 of file BCModel.cxx.

BCModel::BCModel ( const BCModel & bcmodel )

Copy constructor.

Definition at line 52 of file BCModel.cxx.

BCModel::BCModel	(	const std::string &	filename,
		const std::string &	name,
		bool	loadObservables = `true`
	)

Read in MCMC constructor.

Parameters

filename	Path of file holding model.
name	Name of model (file should contain TTree's [name]_mcmc and [name]_parameters. if empty string is given, properly matching TTrees are searched for in the file.
loadObservables	Flag for whether to load observables for file (true; default) or to let user respecify observables.

Definition at line 40 of file BCModel.cxx.

BCModel::~BCModel ( )

virtual

Destructor.

Definition at line 82 of file BCModel.cxx.

Member Function Documentation

virtual double BCModel::APrioriProbability ( const std::vector< double > & parameters )

inlinevirtual

Returns the prior probability.

Parameters

parameters A set of parameter values

Returns: The prior probability p(parameters)

See also: GetPrior(std::vector<double> parameters)

Definition at line 168 of file BCModel.h.

virtual double BCModel::Eval ( const std::vector< double > & parameters )

inlinevirtual

Overloaded function to evaluate integral.

Implements BCIntegrate.

Definition at line 231 of file BCModel.h.

BCH1D& BCModel::GetBCH1DPosteriorDrawingOptions ( )

inline

Returns: BCH1D object for controlling drawing options of posteriors in knowledge update plots.

Definition at line 123 of file BCModel.h.

BCH1D& BCModel::GetBCH1DPriorDrawingOptions ( )

inline

Returns: BCH1D object for controlling drawing options of priors in knowledge update plots.

Definition at line 113 of file BCModel.h.

BCH2D& BCModel::GetBCH2DPosteriorDrawingOptions ( )

inline

Returns: BCH2D object for controlling drawing options of posteriors in knowledge update plots.

Definition at line 128 of file BCModel.h.

BCH2D& BCModel::GetBCH2DPriorDrawingOptions ( )

inline

Returns: BCH2D object for controlling drawing options of priors in knowledge update plots.

Definition at line 118 of file BCModel.h.

BCDataSet* BCModel::GetDataSet ( )

inline

Returns: The data set.

Definition at line 78 of file BCModel.h.

bool BCModel::GetDrawPriorFirst ( ) const

inline

Returns: flag for drawing prior first (true) or posterior first (false) in knowledge update plots.

Definition at line 134 of file BCModel.h.

unsigned BCModel::GetNDataPoints ( ) const

inline

Returns: The number of data points in the current data set.

Definition at line 83 of file BCModel.h.

int BCModel::GetNDoF ( ) const

inline

Returns: The number of degrees of freedom: (number of data points) - (number of free parameters)

Definition at line 89 of file BCModel.h.

BCH1D BCModel::GetPrior ( unsigned index )

virtual

Get prior of a variable as a BCH1D.

If not using factorized priors, first call GetPriorModel()->Marginalize() first.

Parameters

index Index of variable to get prior for.

Returns: BCH1D of prior

Definition at line 234 of file BCModel.cxx.

BCH2D BCModel::GetPrior	(	unsigned	index1,
		unsigned	index2
	)

virtual

Get prior of a pair of variables as a BCH2D.

If not using factorized priors, first call GetPriorModel()->Marginalize() first.

Parameters

index1	Index of variable to get prior for (abcissa).
index2	Index of variable to get prior for (ordinate).

Returns: BCH2D of prior

Definition at line 272 of file BCModel.cxx.

BCPriorModel * BCModel::GetPriorModel	(	bool	prepare = `true`,
		bool	call_likelihood = `false`
	)

virtual

Returns: BCPriorModel.

Definition at line 223 of file BCModel.cxx.

double BCModel::HessianMatrixElement	(	unsigned	index1,
		unsigned	index2,
		const std::vector< double > &	point
	)

Calculates the matrix element of the Hessian matrix.

Parameters

index1	Index of parameter for the first derivative
index2	Index of parameter for the second derivative
point	Vector of parameters for point at which to evaluate.

Returns: The matrix element of the Hessian matrix

Definition at line 136 of file BCModel.cxx.

void BCModel::InitializeMarkovChainTree	(	bool	replacetree = `false`,
		bool	replacefile = `false`
	)

virtual

Initialize the trees containing the Markov chains and parameter info.

No-op if trees already initialized and replace* = false.

Reimplemented from BCEngineMCMC.

Definition at line 117 of file BCModel.cxx.

virtual double BCModel::Likelihood ( const std::vector< double > & params )

inlinevirtual

Returns the likelihood.

Parameters

params A set of parameter values

Returns: The likelihood

Definition at line 185 of file BCModel.h.

virtual double BCModel::LogAPrioriProbability ( const std::vector< double > & parameters )

inlinevirtual

Returns natural logarithm of the prior probability.

Assumes prior factorizes into independent parts set by user for each parameter. Method can be overloaded by the user (especially for nonfactorizable priors)

Parameters

parameters A set of parameter values

Returns: The prior probability p(parameters)

See also: GetPrior(std::vector<double> parameters)

Reimplemented in BCPriorModel, and BCRooInterface.

Definition at line 178 of file BCModel.h.

virtual double BCModel::LogEval ( const std::vector< double > & parameters )

inlinevirtual

Overloaded function to evaluate integral.

Reimplemented from BCIntegrate.

Definition at line 236 of file BCModel.h.

virtual double BCModel::LogLikelihood ( const std::vector< double > & params )

pure virtual

Calculates natural logarithm of the likelihood.

Method needs to be overloaded by the user.

Parameters

params A set of parameter values

Returns: Natural logarithm of the likelihood

Implemented in BCMTF, BCEfficiencyFitter, BCGraphFitter, BCHistogramFitter, BCEmptyModel, BCPriorModel, and BCRooInterface.

double BCModel::LogProbability ( const std::vector< double > & parameters )

virtual

Returns natural logarithm of the a posteriori probability given a set of parameter values.

Parameters

parameters A set of parameter values

Returns: log(posterior probability)

Definition at line 105 of file BCModel.cxx.

double BCModel::LogProbabilityNN ( const std::vector< double > & parameters )

virtual

Returns the natural logarithm of likelihood times prior probability given a set of parameter values.

Parameters

parameters A set of parameter values

Returns: The likelihood times prior probability

Definition at line 88 of file BCModel.cxx.

void BCModel::PrintHessianMatrix ( std::vector< double > parameters )

Prints matrix elements of the Hessian matrix.

Parameters

parameters The parameter values at which point to evaluate the matrix

Definition at line 195 of file BCModel.cxx.

unsigned BCModel::PrintKnowledgeUpdatePlots	(	const std::string &	filename,
		unsigned	hdiv = `1`,
		unsigned	vdiv = `1`,
		bool	call_likelihood = `false`
	)

virtual

Print a comparison of the prior knowledge to the posterior knowledge for each parameter.

Parameters

filename	name of file to print to
hdiv	number of horizontal divisions on page
vdiv	number of vertical divisions on page
call_likelihood	whether to call model likelihood function to prepare for calculating observables

Returns: number of plots printed.

Definition at line 335 of file BCModel.cxx.

void BCModel::PrintShortFitSummary ( )

Prints a short summary of the fit results on the screen.

Definition at line 178 of file BCModel.cxx.

virtual double BCModel::Probability ( const std::vector< double > & parameters )

inlinevirtual

Returns the a posteriori probability given a set of parameter values.

Parameters

parameters A set of parameter values

Returns: The posterior probability

Definition at line 213 of file BCModel.h.

virtual double BCModel::ProbabilityNN ( const std::vector< double > & params )

inlinevirtual

Returns the likelihood times prior probability given a set of parameter values.

Parameters

params A set of parameter values

Returns: The likelihood times prior probability

Definition at line 199 of file BCModel.h.

double BCModel::SamplingFunction ( const std::vector< double > & parameters )

virtual

Sampling function used for importance sampling.

Method needs to be overloaded by the user.

Parameters

parameters A set of parameter values

Returns: The probability density at the parameter values

Definition at line 127 of file BCModel.cxx.

void BCModel::SetDataSet ( BCDataSet * dataset )

inline

Sets the data set.

Model does not own data set!

Parameters

dataset A data set

Definition at line 145 of file BCModel.h.

void BCModel::SetDrawPriorFirst ( bool b = true )

inline

Set drawing of prior first (true) or posterior first (false) for knowledge update plots.

Definition at line 155 of file BCModel.h.

void BCModel::SetKnowledgeUpdateDrawingStyle ( BCAux::BCKnowledgeUpdateDrawingStyle style = BCAux::kKnowledgeUpdateDefaultStyle )

Set default drawing options for knowledge update plots.

Definition at line 484 of file BCModel.cxx.

Member Data Documentation

BCH1D BCModel::fBCH1DPosteriorDrawingOptions

protected

knowledge update plot 1D posterior options.

Definition at line 307 of file BCModel.h.

BCH1D BCModel::fBCH1DPriorDrawingOptions

protected

knowledge update plot 1D prior options.

Definition at line 299 of file BCModel.h.

BCH2D BCModel::fBCH2DPosteriorDrawingOptions

protected

knowledge update plot 2D posterior options.

Definition at line 311 of file BCModel.h.

BCH2D BCModel::fBCH2DPriorDrawingOptions

protected

knowledge update plot 2D prior options.

Definition at line 303 of file BCModel.h.

BCDataSet* BCModel::fDataSet

protected

A data set.

Not owned by the model.

Definition at line 291 of file BCModel.h.

bool BCModel::fDrawPriorFirst

protected

flag for ordering of drawing of prior and posterior in knowledge update plots.

Definition at line 315 of file BCModel.h.

bool BCModel::fFactorizedPrior

protected

flag for whether factorized prior has been used.

Definition at line 319 of file BCModel.h.

BCPriorModel* BCModel::fPriorModel

protected

BCPriorModel object for drawing of knowledge update, and saving of samples according to prior.

Definition at line 295 of file BCModel.h.

The documentation for this class was generated from the following files:

/root/bat/BAT/BCModel.h
/root/bat/src/BCModel.cxx

Public Member Functions

Protected Attributes

Friends

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation