ToyMCImportanceSampler is an extension of the ToyMCSampler for Importance Sampling.

Implementation based on a work by Cranmer, Kreiss, Read (in Preparation)

Definition at line 22 of file ToyMCImportanceSampler.h.

Public Member Functions
	ToyMCImportanceSampler ()=default
	Proof constructor. Do not use.

	ToyMCImportanceSampler (TestStatistic &ts, Int_t ntoys)

	~ToyMCImportanceSampler () override

void	AddImportanceDensity (RooAbsPdf p, const RooArgSet s)
	For importance sampling with multiple densities/snapshots: This is used to check the current Likelihood against Likelihoods from other importance densities apart from the one given as importance snapshot.

void	AddNullDensity (RooAbsPdf p, const RooArgSet s=nullptr)
	The pdf can be nullptr in which case the density from SetPdf() is used.

int	CreateImpDensitiesForOnePOIAdaptively (RooAbsPdf &pdf, const RooArgSet &allPOI, RooRealVar &poi, double nStdDevOverlap=0.5, double poiValueForBackground=0.0)
	poi has to be fitted beforehand. This function expects this to be the muhat value.

int	CreateNImpDensitiesForOnePOI (RooAbsPdf &pdf, const RooArgSet &allPOI, RooRealVar &poi, int n, double poiValueForBackground=0.0)
	n is the number of importance densities

virtual RooAbsData *	GenerateToyData (RooArgSet &paramPoint) const

virtual RooAbsData *	GenerateToyData (RooArgSet &paramPoint, double &weight) const

RooAbsData *	GenerateToyData (RooArgSet &paramPoint, double &weight) const override

virtual RooAbsData *	GenerateToyData (RooArgSet &paramPoint, double &weight, RooAbsPdf &pdf) const
	generates toy data with weight

virtual RooAbsData *	GenerateToyData (RooArgSet &paramPoint, double &weight, std::vector< double > &impNLLs, double &nullNLL) const

virtual RooAbsData *	GenerateToyData (RooArgSet &paramPoint, RooAbsPdf &pdf) const
	generates toy data without weight

virtual RooAbsData *	GenerateToyData (std::vector< double > &weights) const

virtual RooAbsData *	GenerateToyData (std::vector< double > &weights, std::vector< double > &nullNLLs, std::vector< double > &impNLLs) const
	This method generates a toy data set for importance sampling for the given parameter point taking global observables into account.

RooDataSet *	GetSamplingDistributionsSingleWorker (RooArgSet &paramPoint) override
	overwrite GetSamplingDistributionsSingleWorker(paramPoint) with a version that loops over nulls and importance densities, but calls the parent ToyMCSampler::GetSamplingDistributionsSingleWorker(paramPoint).

TClass *	IsA () const override

void	SetApplyVeto (bool b=true)
	When set to true, this sets the weight of all toys to zero that do not have the largest likelihood under the density it was generated compared to the other densities.

virtual void	SetConditionalObservables (const RooArgSet &set)
	set the conditional observables which will be used when creating the NLL so the pdf's will not be normalized on the conditional observables when computing the NLL Since the class use a NLL we need to set the conditional observables if they exist in the model

void	SetDensityToGenerateFromByIndex (unsigned int i, bool fromNull=false)
	specifies the pdf to sample from

void	SetEqualNumToysPerDensity (void)

void	SetExpIncreasingNumToysPerDensity (void)

void	SetParametersForTestStat (const RooArgSet &nullpoi) override
	overwrite from ToyMCSampler

void	SetPdf (RooAbsPdf &pdf) override
	overwrite from ToyMCSampler

void	SetReuseNLL (bool r=true)

void	Streamer (TBuffer &) override

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

Public Member Functions inherited from RooStats::ToyMCSampler
	ToyMCSampler ()
	Proof constructor. Do not use.

	ToyMCSampler (TestStatistic &ts, Int_t ntoys)

	~ToyMCSampler () override

virtual void	AddTestStatistic (TestStatistic *t=nullptr)
	The pdf can be nullptr in which case the density from SetPdf() is used.

virtual SamplingDistribution *	AppendSamplingDistribution (RooArgSet &allParameters, SamplingDistribution *last, Int_t additionalMC)
	Extended interface to append to sampling distribution more samples.

bool	CheckConfig (void)
	Checks for sufficient information to do a GetSamplingDistribution(...).

double	ConfidenceLevel () const override
	Get the Confidence level for the test.

virtual RooArgList *	EvaluateAllTestStatistics (RooAbsData &data, const RooArgSet &poi)
	Evaluate all test statistics, returning result and any detailed output.

double	EvaluateTestStatistic (RooAbsData &data, RooArgSet &nullPOI) override
	Main interface to evaluate the test statistic on a dataset.

virtual double	EvaluateTestStatistic (RooAbsData &data, RooArgSet &nullPOI, int i)
	Main interface to evaluate the test statistic on a dataset.

virtual void	GenerateGlobalObservables (RooAbsPdf &pdf) const
	generate global observables

virtual Int_t	GetNToys (void)

std::string	GetSamplingDistName (void)

SamplingDistribution *	GetSamplingDistribution (RooArgSet &paramPoint) override
	main interface

virtual RooDataSet *	GetSamplingDistributions (RooArgSet &paramPoint)
	Use for serial and parallel runs.

virtual TestStatistic *	GetTestStatistic (unsigned int i) const

TestStatistic *	GetTestStatistic (void) const override
	Get the TestStatistic.

void	Initialize (RooAbsArg &, RooArgSet &, RooArgSet &) override
	Common Initialization.

TClass *	IsA () const override

virtual void	SetAsimovNuisancePar (bool i=true)

void	SetConfidenceLevel (double cl) override
	set the confidence level for the interval (eg. 0.95 for a 95% Confidence Interval)

virtual void	SetExpectedNuisancePar (bool i=true)

void	SetGenerateAutoBinned (bool autoBinned=true)
	set auto binned generation (=> see RooFit::AutoBinned() option)

void	SetGenerateBinned (bool binned=true)
	control to use bin data generation (=> see RooFit::AllBinned() option)

void	SetGenerateBinnedTag (const char *binnedTag="")
	name of the tag for individual components to be generated binned (=> see RooFit::GenBinned() option)

void	SetGlobalObservables (const RooArgSet &o) override
	specify the conditional observables

void	SetMaxToys (double t)
	This option forces a maximum number of total toys.

virtual void	SetNEventsPerToy (const Int_t nevents)
	Forces the generation of exactly `n` events even for extended PDFs.

virtual void	SetNToys (const Int_t ntoy)

void	SetNuisanceParameters (const RooArgSet &np) override
	specify the nuisance parameters (eg. the rest of the parameters)

void	SetObservables (const RooArgSet &o) override
	specify the observables in the dataset (needed to evaluate the test statistic)

void	SetParametersForTestStat (const RooArgSet &nullpoi) override
	Set the Pdf, add to the workspace if not already there.

void	SetPdf (RooAbsPdf &pdf) override
	Set the Pdf, add to the workspace if not already there.

void	SetPriorNuisance (RooAbsPdf *pdf) override
	How to randomize the prior. Set to nullptr to deactivate randomization.

void	SetProtoData (const RooDataSet *d)

void	SetSamplingDistName (const char *name) override
	Set the name of the sampling distribution used for plotting.

void	SetTestSize (double size) override
	set the size of the test (rate of Type I error) ( Eg. 0.05 for a 95% Confidence Interval)

void	SetTestStatistic (TestStatistic *t) override
	Set the TestStatistic (want the argument to be a function of the data & parameter points.

virtual void	SetTestStatistic (TestStatistic *testStatistic, unsigned int i)
	Set the TestStatistic (want the argument to be a function of the data & parameter points.

void	SetToysBothTails (double toys, double low_threshold, double high_threshold)

void	SetToysLeftTail (double toys, double threshold)

void	SetToysRightTail (double toys, double threshold)

void	SetUseMultiGen (bool flag)

void	Streamer (TBuffer &) override

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

Public Member Functions inherited from RooStats::TestStatSampler
virtual	~TestStatSampler ()

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

Static Public Member Functions
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

Static Public Member Functions inherited from RooStats::ToyMCSampler
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

static void	SetAlwaysUseMultiGen (bool flag)

Static Public Member Functions inherited from RooStats::TestStatSampler
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

Protected Member Functions
void	ClearCache () override
	helper method for clearing the cache

Protected Member Functions inherited from RooStats::ToyMCSampler
const RooArgList *	EvaluateAllTestStatistics (RooAbsData &data, const RooArgSet &poi, DetailedOutputAggregator &detOutAgg)

std::unique_ptr< RooAbsData >	Generate (RooAbsPdf &pdf, RooArgSet &observables, const RooDataSet *protoData=nullptr, int forceEvents=0) const
	helper for GenerateToyData

Protected Attributes
bool	fApplyVeto = true

RooArgSet	fConditionalObs
	set of conditional observables

bool	fGenerateFromNull = true

std::vector< std::unique_ptr< RooAbsReal > >	fImpNLLs
	!

std::vector< RooAbsPdf * >	fImportanceDensities

std::vector< const RooArgSet * >	fImportanceSnapshots

unsigned int	fIndexGenDensity = 0

std::vector< RooAbsPdf * >	fNullDensities
	support multiple null densities

std::vector< std::unique_ptr< RooAbsReal > >	fNullNLLs
	!

std::vector< const RooArgSet * >	fNullSnapshots

bool	fReuseNLL = true

toysStrategies	fToysStrategy = EQUALTOYSPERDENSITY

Protected Attributes inherited from RooStats::ToyMCSampler
std::unique_ptr< RooArgSet >	_allVars
	!

std::unique_ptr< RooAbsPdf::GenSpec >	_gs1
	! GenSpec #1

std::unique_ptr< RooAbsPdf::GenSpec >	_gs2
	! GenSpec #2

std::unique_ptr< RooAbsPdf::GenSpec >	_gs3
	! GenSpec #3

std::unique_ptr< RooAbsPdf::GenSpec >	_gs4
	! GenSpec #4

std::vector< std::unique_ptr< RooAbsPdf::GenSpec > >	_gsList
	!

std::vector< std::unique_ptr< RooArgSet > >	_obsList
	!

std::vector< RooAbsPdf * >	_pdfList
	! We don't own those objects

double	fAdaptiveHighLimit

double	fAdaptiveLowLimit
	tails

bool	fExpectedNuisancePar
	whether to use expectation values for nuisance parameters (ie Asimov data set)

bool	fGenerateAutoBinned = true

bool	fGenerateBinned = false

TString	fGenerateBinnedTag = ""

const RooArgSet *	fGlobalObservables = nullptr

double	fMaxToys
	maximum no of toys (taking weights into account, therefore double)

Int_t	fNEvents = 0
	number of events per toy (may be ignored depending on settings)

Int_t	fNToys
	number of toys to generate

NuisanceParametersSampler *	fNuisanceParametersSampler = nullptr
	!

const RooArgSet *	fNuisancePars = nullptr

const RooArgSet *	fObservables = nullptr

std::unique_ptr< const RooArgSet >	fParametersForTestStat

RooAbsPdf *	fPdf = nullptr
	densities, snapshots, and test statistics to reweight to

RooAbsPdf *	fPriorNuisance = nullptr
	prior pdf for nuisance parameters

const RooDataSet *	fProtoData = nullptr
	in dev

std::string	fSamplingDistName
	name of the model

double	fSize = 0.05

std::vector< TestStatistic * >	fTestStatistics

double	fToysInTails = 0.0
	minimum no of toys in tails for adaptive sampling (taking weights into account, therefore double) Default: 0.0 which means no adaptive sampling

bool	fUseMultiGen = false
	Use PrepareMultiGen?

Additional Inherited Members
Static Protected Attributes inherited from RooStats::ToyMCSampler
static bool	fgAlwaysUseMultiGen = false
	Use PrepareMultiGen always.

#include <RooStats/ToyMCImportanceSampler.h>

Inheritance diagram for RooStats::ToyMCImportanceSampler:

[legend]

Constructor & Destructor Documentation

◆ ToyMCImportanceSampler() [1/2]

RooStats::ToyMCImportanceSampler::ToyMCImportanceSampler ( )

default

Proof constructor. Do not use.

◆ ToyMCImportanceSampler() [2/2]

RooStats::ToyMCImportanceSampler::ToyMCImportanceSampler	(	TestStatistic &	ts,
		Int_t	ntoys
	)

inline

Definition at line 28 of file ToyMCImportanceSampler.h.

◆ ~ToyMCImportanceSampler()

RooStats::ToyMCImportanceSampler::~ToyMCImportanceSampler ( )

override

Definition at line 37 of file ToyMCImportanceSampler.cxx.

Member Function Documentation

◆ AddImportanceDensity()

void RooStats::ToyMCImportanceSampler::AddImportanceDensity	(	RooAbsPdf *	p,
		const RooArgSet *	s
	)

inline

For importance sampling with multiple densities/snapshots: This is used to check the current Likelihood against Likelihoods from other importance densities apart from the one given as importance snapshot.

The pdf can be nullptr in which case the density from SetImportanceDensity() is used. The snapshot is also optional.

Definition at line 65 of file ToyMCImportanceSampler.h.

◆ AddNullDensity()

void RooStats::ToyMCImportanceSampler::AddNullDensity	(	RooAbsPdf *	p,
		const RooArgSet *	s = `nullptr`
	)

inline

The pdf can be nullptr in which case the density from SetPdf() is used.

The snapshot and TestStatistic is also optional.

Definition at line 86 of file ToyMCImportanceSampler.h.

◆ Class()

static TClass * RooStats::ToyMCImportanceSampler::Class ( )

static

Returns: TClass describing this class

◆ Class_Name()

static const char * RooStats::ToyMCImportanceSampler::Class_Name ( )

static

Returns: Name of this class

◆ Class_Version()

static constexpr Version_t RooStats::ToyMCImportanceSampler::Class_Version ( )

inlinestaticconstexpr

Returns: Version of this class

Definition at line 181 of file ToyMCImportanceSampler.h.

◆ ClearCache()

void RooStats::ToyMCImportanceSampler::ClearCache ( void )

overrideprotectedvirtual

helper method for clearing the cache

Reimplemented from RooStats::ToyMCSampler.

Definition at line 44 of file ToyMCImportanceSampler.cxx.

◆ CreateImpDensitiesForOnePOIAdaptively()

int RooStats::ToyMCImportanceSampler::CreateImpDensitiesForOnePOIAdaptively	(	RooAbsPdf &	pdf,
		const RooArgSet &	allPOI,
		RooRealVar &	poi,
		double	nStdDevOverlap = `0.5`,
		double	poiValueForBackground = `0.0`
	)

poi has to be fitted beforehand. This function expects this to be the muhat value.

Definition at line 428 of file ToyMCImportanceSampler.cxx.

◆ CreateNImpDensitiesForOnePOI()

int RooStats::ToyMCImportanceSampler::CreateNImpDensitiesForOnePOI	(	RooAbsPdf &	pdf,
		const RooArgSet &	allPOI,
		RooRealVar &	poi,
		int	n,
		double	poiValueForBackground = `0.0`
	)

n is the number of importance densities

Definition at line 451 of file ToyMCImportanceSampler.cxx.

◆ DeclFileName()

static const char * RooStats::ToyMCImportanceSampler::DeclFileName ( )

inlinestatic

Returns: Name of the file containing the class declaration

Definition at line 181 of file ToyMCImportanceSampler.h.

◆ GenerateToyData() [1/8]

virtual RooAbsData * RooStats::ToyMCSampler::GenerateToyData ( RooArgSet & paramPoint ) const

inlinevirtual

Reimplemented from RooStats::ToyMCSampler.

Definition at line 108 of file ToyMCSampler.h.

◆ GenerateToyData() [2/8]

virtual RooAbsData * RooStats::ToyMCSampler::GenerateToyData	(	RooArgSet &	paramPoint,
		double &	weight
	)		const

inlinevirtual

Reimplemented from RooStats::ToyMCSampler.

Definition at line 112 of file ToyMCSampler.h.

◆ GenerateToyData() [3/8]

RooAbsData * RooStats::ToyMCImportanceSampler::GenerateToyData	(	RooArgSet &	paramPoint,
		double &	weight
	)		const

overridevirtual

Reimplemented from RooStats::ToyMCSampler.

Definition at line 129 of file ToyMCImportanceSampler.cxx.

◆ GenerateToyData() [4/8]

RooAbsData * RooStats::ToyMCSampler::GenerateToyData	(	RooArgSet &	paramPoint,
		double &	weight,
		RooAbsPdf &	pdf
	)		const

virtual

generates toy data with weight

This method generates a toy data set for the given parameter point taking global observables into account.

The values of the generated global observables remain in the pdf's variables. They have to have those values for the subsequent evaluation of the test statistics.

Reimplemented from RooStats::ToyMCSampler.

Definition at line 111 of file ToyMCSampler.cxx.

◆ GenerateToyData() [5/8]

RooAbsData * RooStats::ToyMCImportanceSampler::GenerateToyData	(	RooArgSet &	paramPoint,
		double &	weight,
		std::vector< double > &	impNLLs,
		double &	nullNLL
	)		const

virtual

Definition at line 171 of file ToyMCImportanceSampler.cxx.

◆ GenerateToyData() [6/8]

virtual RooAbsData * RooStats::ToyMCSampler::GenerateToyData	(	RooArgSet &	paramPoint,
		RooAbsPdf &	pdf
	)		const

inlinevirtual

generates toy data without weight

Reimplemented from RooStats::ToyMCSampler.

Definition at line 103 of file ToyMCSampler.h.

◆ GenerateToyData() [7/8]

RooAbsData * RooStats::ToyMCImportanceSampler::GenerateToyData ( std::vector< double > & weights ) const

virtual

Definition at line 210 of file ToyMCImportanceSampler.cxx.

◆ GenerateToyData() [8/8]

RooAbsData * RooStats::ToyMCImportanceSampler::GenerateToyData	(	std::vector< double > &	weights,
		std::vector< double > &	nullNLLs,
		std::vector< double > &	impNLLs
	)		const

virtual

This method generates a toy data set for importance sampling for the given parameter point taking global observables into account.

The values of the generated global observables remain in the pdf's variables. They have to have those values for the subsequent evaluation of the test statistics.

Definition at line 234 of file ToyMCImportanceSampler.cxx.

◆ GetSamplingDistributionsSingleWorker()

RooDataSet * RooStats::ToyMCImportanceSampler::GetSamplingDistributionsSingleWorker ( RooArgSet & paramPoint )

overridevirtual

overwrite GetSamplingDistributionsSingleWorker(paramPoint) with a version that loops over nulls and importance densities, but calls the parent ToyMCSampler::GetSamplingDistributionsSingleWorker(paramPoint).

Reimplemented from RooStats::ToyMCSampler.

Definition at line 53 of file ToyMCImportanceSampler.cxx.

◆ IsA()

TClass * RooStats::ToyMCImportanceSampler::IsA ( ) const

inlineoverridevirtual

Returns: TClass describing current object

Reimplemented from RooStats::TestStatSampler.

Definition at line 181 of file ToyMCImportanceSampler.h.

◆ SetApplyVeto()

void RooStats::ToyMCImportanceSampler::SetApplyVeto ( bool b = true )

inline

When set to true, this sets the weight of all toys to zero that do not have the largest likelihood under the density it was generated compared to the other densities.

Definition at line 127 of file ToyMCImportanceSampler.h.

◆ SetConditionalObservables()

virtual void RooStats::ToyMCImportanceSampler::SetConditionalObservables ( const RooArgSet & set )

inlinevirtual

set the conditional observables which will be used when creating the NLL so the pdf's will not be normalized on the conditional observables when computing the NLL Since the class use a NLL we need to set the conditional observables if they exist in the model

Definition at line 134 of file ToyMCImportanceSampler.h.

◆ SetDensityToGenerateFromByIndex()

void RooStats::ToyMCImportanceSampler::SetDensityToGenerateFromByIndex	(	unsigned int	i,
		bool	fromNull = `false`
	)

inline

specifies the pdf to sample from

Definition at line 45 of file ToyMCImportanceSampler.h.

◆ SetEqualNumToysPerDensity()

void RooStats::ToyMCImportanceSampler::SetEqualNumToysPerDensity ( void )

inline

Definition at line 151 of file ToyMCImportanceSampler.h.

◆ SetExpIncreasingNumToysPerDensity()

void RooStats::ToyMCImportanceSampler::SetExpIncreasingNumToysPerDensity ( void )

inline

Definition at line 152 of file ToyMCImportanceSampler.h.

◆ SetParametersForTestStat()

void RooStats::ToyMCImportanceSampler::SetParametersForTestStat ( const RooArgSet & nullpoi )

inlineoverridevirtual

overwrite from ToyMCSampler

Implements RooStats::TestStatSampler.

Definition at line 112 of file ToyMCImportanceSampler.h.

◆ SetPdf()

void RooStats::ToyMCImportanceSampler::SetPdf ( RooAbsPdf & pdf )

inlineoverridevirtual

overwrite from ToyMCSampler

Implements RooStats::TestStatSampler.

Definition at line 102 of file ToyMCImportanceSampler.h.

◆ SetReuseNLL()

void RooStats::ToyMCImportanceSampler::SetReuseNLL ( bool r = true )

inline

Definition at line 129 of file ToyMCImportanceSampler.h.

◆ Streamer()

void RooStats::ToyMCImportanceSampler::Streamer ( TBuffer & )

overridevirtual

Reimplemented from RooStats::TestStatSampler.

◆ StreamerNVirtual()

void RooStats::ToyMCImportanceSampler::StreamerNVirtual ( TBuffer & ClassDef_StreamerNVirtual_b )

inline

Definition at line 181 of file ToyMCImportanceSampler.h.

Member Data Documentation

◆ fApplyVeto

bool RooStats::ToyMCImportanceSampler::fApplyVeto = true

protected

Definition at line 161 of file ToyMCImportanceSampler.h.

◆ fConditionalObs

RooArgSet RooStats::ToyMCImportanceSampler::fConditionalObs

protected

set of conditional observables

Definition at line 163 of file ToyMCImportanceSampler.h.

◆ fGenerateFromNull

bool RooStats::ToyMCImportanceSampler::fGenerateFromNull = true

protected

Definition at line 160 of file ToyMCImportanceSampler.h.

◆ fImpNLLs

std::vector<std::unique_ptr<RooAbsReal> > RooStats::ToyMCImportanceSampler::fImpNLLs

mutableprotected

!

Definition at line 178 of file ToyMCImportanceSampler.h.

◆ fImportanceDensities

std::vector<RooAbsPdf*> RooStats::ToyMCImportanceSampler::fImportanceDensities

protected

Definition at line 170 of file ToyMCImportanceSampler.h.

◆ fImportanceSnapshots

std::vector<const RooArgSet*> RooStats::ToyMCImportanceSampler::fImportanceSnapshots

protected

Definition at line 171 of file ToyMCImportanceSampler.h.

◆ fIndexGenDensity

unsigned int RooStats::ToyMCImportanceSampler::fIndexGenDensity = 0

protected

Definition at line 159 of file ToyMCImportanceSampler.h.

◆ fNullDensities

std::vector<RooAbsPdf*> RooStats::ToyMCImportanceSampler::fNullDensities

protected

support multiple null densities

Definition at line 166 of file ToyMCImportanceSampler.h.

◆ fNullNLLs

std::vector<std::unique_ptr<RooAbsReal> > RooStats::ToyMCImportanceSampler::fNullNLLs

mutableprotected

!

Definition at line 177 of file ToyMCImportanceSampler.h.

◆ fNullSnapshots

std::vector<const RooArgSet*> RooStats::ToyMCImportanceSampler::fNullSnapshots

mutableprotected

Definition at line 167 of file ToyMCImportanceSampler.h.

◆ fReuseNLL

bool RooStats::ToyMCImportanceSampler::fReuseNLL = true

protected

Definition at line 173 of file ToyMCImportanceSampler.h.

◆ fToysStrategy

toysStrategies RooStats::ToyMCImportanceSampler::fToysStrategy = EQUALTOYSPERDENSITY

protected

Definition at line 175 of file ToyMCImportanceSampler.h.

Libraries for RooStats::ToyMCImportanceSampler:

[legend]

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

roofit/roostats/inc/RooStats/ToyMCImportanceSampler.h
roofit/roostats/src/ToyMCImportanceSampler.cxx

Public Member Functions

Static Public Member Functions

Protected Member Functions

Protected Attributes

Additional Inherited Members

Constructor & Destructor Documentation

◆ ToyMCImportanceSampler() [1/2]

◆ ToyMCImportanceSampler() [2/2]

◆ ~ToyMCImportanceSampler()

Member Function Documentation

◆ AddImportanceDensity()

◆ AddNullDensity()

◆ Class()

◆ Class_Name()

◆ Class_Version()

◆ ClearCache()

◆ CreateImpDensitiesForOnePOIAdaptively()

◆ CreateNImpDensitiesForOnePOI()

◆ DeclFileName()

◆ GenerateToyData() [1/8]

◆ GenerateToyData() [2/8]

◆ GenerateToyData() [3/8]

◆ GenerateToyData() [4/8]

◆ GenerateToyData() [5/8]

◆ GenerateToyData() [6/8]

◆ GenerateToyData() [7/8]

◆ GenerateToyData() [8/8]

◆ GetSamplingDistributionsSingleWorker()

◆ IsA()

◆ SetApplyVeto()

◆ SetConditionalObservables()

◆ SetDensityToGenerateFromByIndex()

◆ SetEqualNumToysPerDensity()

◆ SetExpIncreasingNumToysPerDensity()

◆ SetParametersForTestStat()

◆ SetPdf()

◆ SetReuseNLL()

◆ Streamer()

◆ StreamerNVirtual()

Member Data Documentation

◆ fApplyVeto

◆ fConditionalObs

◆ fGenerateFromNull

◆ fImpNLLs

◆ fImportanceDensities

◆ fImportanceSnapshots

◆ fIndexGenDensity

◆ fNullDensities

◆ fNullNLLs

◆ fNullSnapshots

◆ fReuseNLL

◆ fToysStrategy