A class for fitting histograms with functions. More...

#include <BCEfficiencyFitter.h>

Inheritance diagram for BCEfficiencyFitter:

Collaboration diagram for BCEfficiencyFitter:

Classes
class	ToyDataInterface
	Abstract class which doesn't do anything but offers the right interface to allow calculation the distribution of any statistic. More...

Public Types
Enumerator
enum	DataPointType { kDataPointRMS = 0, kDataPointSmallestInterval = 1, kDataPointCentralInterval = 2 }

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...

Public Member Functions
Constructors and destructor
	BCEfficiencyFitter (const TH1 &trials, const TH1 &successes, const TF1 &func, const std::string &name="efficiency_fitter_model")
	Constructor. More...

virtual	~BCEfficiencyFitter ()
	The default destructor. More...

Member functions (get)
TH1 &	GetTrials ()

TH1 &	GetSuccesses ()

bool	GetUncertainties (int n, int k, double p, double &xexp, double &xmin, double &xmax)
	Calculates the central value and the lower and upper limits for a given probability. More...

Member functions (set)
void	SetDataPointType (DataPointType type)
	Set type of point to be used to plot the efficiency data.

Member functions (miscellaneous methods)
virtual double	LogLikelihood (const std::vector< double > &parameters)
	The log of the prior probability. More...

virtual void	Fit ()
	Performs the fit. More...

virtual void	DrawData (bool flaglegend=false)
	Draw the data in the current pad. More...

virtual void	DrawFit (const std::string &options="", bool flaglegend=false)
	Draw the fit in the current pad. More...

double	CalculatePValueFast (const std::vector< double > &par, BCEfficiencyFitter::ToyDataInterface *callback, unsigned nIterations=100000)
	Calculate the p-value using fast-MCMC. More...

double	CalculatePValueFast (const std::vector< double > &par, unsigned nIterations=100000)
	Calculate the p-value using fast-MCMC. More...

Public Member Functions inherited from BCFitter
virtual void	MCMCUserInitialize ()
	Create enough TF1 copies for thread safety.

	BCFitter (const TF1 &f, const std::string &name="fitter_model")
	Constructor. More...

virtual	~BCFitter ()=0
	The default destructor. More...

TF1 &	GetFitFunction ()
	Get fit function. More...

TH2 *	GetGraphicalErrorBandXY (double level=.68, int nsmooth=0, bool overcoverage=true) const

const TH2 &	GetErrorBandXY () const

std::vector< double >	GetErrorBand (double level) const
	Returns a vector of y-values at a certain probability level. More...

TGraph *	GetErrorBandGraph (double level1, double level2) const

TGraph *	GetFitFunctionGraph (const std::vector< double > &parameters)

TGraph *	GetFitFunctionGraph ()

TGraph *	GetFitFunctionGraph (const std::vector< double > &parameters, double xmin, double xmax, int n=1000)

void	FixDataAxis (unsigned int index, bool fixed)
	Toggle the data axis defined by `index` to be fixed. More...

bool	GetFixedDataAxis (unsigned int index) const

double	GetPValue () const

void	SetErrorBandContinuous (bool flag)
	Sets the error band flag to continuous function.

void	SetErrorBandExtensionLowEdgeX (double extension)
	Extends the lower x Edge of th errorband by -extension.

void	SetErrorBandExtensionUpEdgeX (double extension)
	Extends the lower x Edge of th errorband by +extension.

void	SetErrorBandExtensionLowEdgeY (double extension)
	Extends the lower y Edge of th errorband by -extension.

void	SetErrorBandExtensionUpEdgeY (double extension)
	Extends the lower y Edge of th errorband by +extension.

void	SetFillErrorBand (bool flag=true)
	Turn on or off the filling of the error band during the MCMC run. More...

void	UnsetFillErrorBand ()
	Turn off filling of the error band during the MCMC run. More...

void	SetFitFunctionIndexX (int index)
	Sets index of the x values in function fits. More...

void	SetFitFunctionIndexY (int index)
	Sets index of the y values in function fits. More...

void	SetFitFunctionIndices (int indexx, int indexy)
	Sets indices of the x and y values in function fits. More...

void	SetFlagIntegration (bool flag)
	Sets the flag for integration. More...

virtual double	FitFunction (const std::vector< double > &x, const std::vector< double > &parameters)
	Defines a fit function. More...

double	Integral (const std::vector< double > &parameters, double xmin, double xmax)
	Compute the integral of the fit function between xmin and xmax. More...

virtual void	MCMCUserIterationInterface ()
	Overloaded from BCEngineMCMC.

virtual void	MarginalizePreprocess ()
	Overloaded from BCIntegrate. More...

void	FillErrorBand ()
	Fill error band histogram for current iteration. More...

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

Public Member Functions inherited from BCModel
	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...

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

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...

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	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	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	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...

Additional Inherited Members
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 BCFitter
virtual double	GraphCorrection (unsigned) const
	Take care of bin width when creating a graph from the fit function.

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...

Protected Attributes inherited from BCFitter
bool	fErrorBandContinuous
	A flag for single point evaluation of the error "band".

double	fErrorBandExtensionLowEdgeX
	extends the lower edge of x range by the given value

double	fErrorBandExtensionLowEdgeY
	extends the upper edge of y range by the given value

double	fErrorBandExtensionUpEdgeX
	extends the upper edge of x range by the given value

double	fErrorBandExtensionUpEdgeY
	extends the upper edge of y range by the given value

unsigned	fErrorBandNbinsX
	Number of X bins of the error band histogram.

unsigned	fErrorBandNbinsY
	Number of Y bins of the error band histogram.

std::vector< double >	fErrorBandX
	The x positions where the error is calculated.

TH2D	fErrorBandXY
	The error band histogram.

int	fFitFunctionIndexX
	The index for function fits in x direction.

int	fFitFunctionIndexY
	The index for function fits in y direction.

BCDataSet	fFitterDataSet
	Needed for uncertainty propagation.

bool	fFlagFillErrorBand
	Flag whether or not to fill the error band.

bool	fFlagIntegration
	Flag for using the ROOT TH1::Integral method (true), or linear interpolation (false)

double	fPValue
	p value for goodness of fit

Protected Attributes inherited from BCModel
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...

Detailed Description

A class for fitting histograms with functions.

Author: Daniel Kollar; Kevin Kröninger

Version: 1.0

Date: 11.2008

This class allows fitting of efficiencies defined as a ratio of two TH1D histograms using a TF1 function. It uses binomial probabilities calculated based on the number of entries in histograms. This is only applicable if the numerator is a subset of the denominator.

Definition at line 39 of file BCEfficiencyFitter.h.

Member Enumeration Documentation

enum BCEfficiencyFitter::DataPointType

Enumerator
kDataPointRMS	Draw mean and standard deviation.
kDataPointSmallestInterval	Draw mean and smallest 68% interval.
kDataPointCentralInterval	Draw mean and central 68% interval.

Definition at line 46 of file BCEfficiencyFitter.h.

Constructor & Destructor Documentation

BCEfficiencyFitter::BCEfficiencyFitter	(	const TH1 &	trials,
		const TH1 &	successes,
		const TF1 &	func,
		const std::string &	name = `"efficiency_fitter_model"`
	)

Constructor.

Parameters

trials	The histogram with the number of trials.
successes	The histogram with the number of successes.
func	The fit function.
name	name fo the model

Definition at line 30 of file BCEfficiencyFitter.cxx.

BCEfficiencyFitter::~BCEfficiencyFitter ( )

virtual

The default destructor.

Definition at line 75 of file BCEfficiencyFitter.cxx.

Member Function Documentation

double BCEfficiencyFitter::CalculatePValueFast	(	const std::vector< double > &	par,
		BCEfficiencyFitter::ToyDataInterface *	callback,
		unsigned	nIterations = `100000`
	)

Calculate the p-value using fast-MCMC.

In every iteration, a new toy data set is created. By providing a suitable implementation of ToyDataInterface, the user can calculate the distribution of an arbitrary statistic. Each toy data set as well as the expected values for the parameter values are passed on to the interface.

Parameters

par	A set of parameter values
callback	requires class with operator(...) defined.
nIterations	number of toy data sets generated by the Markov chain

Returns: p-value.

Definition at line 280 of file BCEfficiencyFitter.cxx.

double BCEfficiencyFitter::CalculatePValueFast	(	const std::vector< double > &	par,
		unsigned	nIterations = `100000`
	)

Calculate the p-value using fast-MCMC.

Parameters

par	A set of parameter values
nIterations	number of pseudo experiments generated by the Markov chain

Returns: p-value.

Definition at line 273 of file BCEfficiencyFitter.cxx.

void BCEfficiencyFitter::DrawData ( bool flaglegend = false )

virtual

Draw the data in the current pad.

Definition at line 138 of file BCEfficiencyFitter.cxx.

void BCEfficiencyFitter::DrawFit	(	const std::string &	options = `""`,
		bool	flaglegend = `false`
	)

virtual

Draw the fit in the current pad.

Implements BCFitter.

Definition at line 193 of file BCEfficiencyFitter.cxx.

void BCEfficiencyFitter::Fit ( )

virtual

Performs the fit.

Returns: Success of action.

Implements BCFitter.

Definition at line 118 of file BCEfficiencyFitter.cxx.

TH1& BCEfficiencyFitter::GetSuccesses ( )

inline

Returns: The histogram with the number of successes.

Definition at line 100 of file BCEfficiencyFitter.h.

TH1& BCEfficiencyFitter::GetTrials ( )

inline

Returns: The histogram with the number of trials

Definition at line 95 of file BCEfficiencyFitter.h.

bool BCEfficiencyFitter::GetUncertainties	(	int	n,
		int	k,
		double	p,
		double &	xexp,
		double &	xmin,
		double &	xmax
	)

Calculates the central value and the lower and upper limits for a given probability.

Parameters

n	n for the binomial.
k	k for the binomial.
p	The central probability defining the limits.
xexp	The central value.
xmin	The lower limit.
xmax	The upper limit.

Returns: Success of action.

Definition at line 380 of file BCEfficiencyFitter.cxx.

double BCEfficiencyFitter::LogLikelihood ( const std::vector< double > & parameters )

virtual

The log of the prior probability.

Overloaded from BCModel.

Parameters

parameters	A vector of doubles containing the parameter values. The log of the conditional probability. Overloaded from BCModel.
parameters	A vector of doubles containing the parameter values.

Implements BCModel.

Definition at line 78 of file BCEfficiencyFitter.cxx.

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

/root/bat/models/base/BCEfficiencyFitter.h
/root/bat/models/base/BCEfficiencyFitter.cxx

Classes

Public Types

Public Member Functions

Additional Inherited Members

Detailed Description

Member Enumeration Documentation

Constructor & Destructor Documentation

Member Function Documentation