BayesianCalculator is a concrete implementation of IntervalCalculator, providing the computation of a credible interval using a Bayesian method.

The class works only for one single parameter of interest and it integrates the likelihood function with the given prior probability density function to compute the posterior probability. The result of the class is a one dimensional interval (class SimpleInterval ), which is obtained from inverting the cumulative posterior distribution. This calculator works then only for model with a single parameter of interest. The model can instead have several nuisance parameters which are integrated (marginalized) in the computation of the posterior function. The integration and normalization of the posterior is computed using numerical integration methods provided by ROOT. See the MCMCCalculator for model with multiple parameters of interest.

The interface allows one to construct the class by passing the data set, probability density function for the model, the prior functions and then the parameter of interest to scan. The nuisance parameters can also be passed to be marginalized when computing the posterior. Alternatively, the class can be constructed by passing the data and the ModelConfig containing all the needed information (model pdf, prior pdf, parameter of interest, nuisance parameters, etc..)

After configuring the calculator, one only needs to ask GetInterval(), which will return an SimpleInterval object. By default the extrem of the integral are obtained by inverting directly the cumulative posterior distribution. By using the method SetScanOfPosterior(nbins) the interval is then obtained by scanning the posterior function in the given number of points. The first method is in general faster but it requires an integration one extra dimension ( in the poi in addition to the nuisance parameters), therefore in some case it can be less robust.

The class can also return the posterior function (method GetPosteriorFunction) or if needed the normalized posterior function (the posterior pdf) (method GetPosteriorPdf). A posterior plot is also obtained using the GetPosteriorPlot method.

The class allows to use different integration methods for integrating in (marginalizing) the nuisances and in the poi. All the numerical integration methods of ROOT can be used via the method SetIntegrationType (see more in the documentation of this method).

Calculator estimating a credible interval using the Bayesian procedure. The calculator computes given the model the posterior distribution and estimates the credible interval from the given function.

Definition at line 37 of file BayesianCalculator.h.

Public Member Functions
	BayesianCalculator ()
	constructor

	BayesianCalculator (RooAbsData &data, ModelConfig &model)
	Constructor from a data set and a ModelConfig model pdf, poi and nuisances will be taken from the ModelConfig.

	BayesianCalculator (RooAbsData &data, RooAbsPdf &pdf, const RooArgSet &POI, RooAbsPdf &priorPdf, const RooArgSet *nuisanceParameters=nullptr)
	Constructor from data set, model pdf, parameter of interests and prior pdf If nuisance parameters are given they will be integrated according either to the prior or their constraint term included in the model.

	~BayesianCalculator () override
	destructor

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

void	ForceNuisancePdf (RooAbsPdf &pdf)

SimpleInterval *	GetInterval () const override
	compute the interval.

double	GetMode () const
	return the mode (most probable value of the posterior function)

RooAbsReal *	GetPosteriorFunction () const
	return posterior function (object is managed by the BayesianCalculator class)

TH1 *	GetPosteriorHistogram () const
	return the approximate posterior as histogram (TH1 object). Note the object is managed by the BayesianCalculator class

RooAbsPdf *	GetPosteriorPdf () const
	return posterior pdf (object is managed by the user)

RooPlot *	GetPosteriorPlot (bool norm=false, double precision=0.01) const
	get the plot with option to get it normalized

TClass *	IsA () const override

void	SetBrfPrecision (double precision)
	set the precision of the Root Finder

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

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

void	SetData (RooAbsData &data) override
	Set the DataSet ( add to the workspace if not already there ?)

virtual void	SetGlobalObservables (const RooArgSet &set)
	set the global observables which will be used when creating the NLL so the constraint pdf's will be normalized correctly on the global observables when computing the NLL

void	SetIntegrationType (const char *type)
	set the integration type (possible type are) :

void	SetLeftSideTailFraction (double leftSideFraction)
	set the fraction of probability content on the left tail Central limits use 0.5 (default case) for upper limits it is 0 and 1 for lower limit For shortest intervals a negative value (i.e.

void	SetModel (const ModelConfig &model) override
	set the model via the ModelConfig

virtual void	SetNuisanceParameters (const RooArgSet &set)
	specify the nuisance parameters (eg. the rest of the parameters)

virtual void	SetNumIters (Int_t numIters)
	set the number of iterations when running a MC integration algorithm If not set use default algorithmic values In case of ToyMC sampling of the nuisance the value is 100 In case of using the GSL MCintegrations types the default value is defined in ROOT::Math::IntegratorMultiDimOptions::DefaultNCalls()

virtual void	SetParameters (const RooArgSet &set)
	specify the parameters of interest in the interval

virtual void	SetPriorPdf (RooAbsPdf &pdf)
	Set only the Prior Pdf.

void	SetScanOfPosterior (int nbin=100)
	use directly the approximate posterior function obtained by binning it in nbins by default the cdf is used by integrating the posterior if a value of nbin <= 0 the cdf function will be used

void	SetShortestInterval ()
	set the Bayesian calculator to compute the shortest interval (default is central interval) to switch off SetLeftSideTailFraction to the right value

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

double	Size () const override
	Get the size of the test (eg. rate of Type I error)

void	Streamer (TBuffer &) override

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

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

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

Public Member Functions inherited from TNamed
	TNamed ()

	TNamed (const char name, const char title)

	TNamed (const TNamed &named)
	TNamed copy ctor.

	TNamed (const TString &name, const TString &title)

virtual	~TNamed ()
	TNamed destructor.

void	Clear (Option_t *option="") override
	Set name and title to empty strings ("").

TObject *	Clone (const char *newname="") const override
	Make a clone of an object using the Streamer facility.

Int_t	Compare (const TObject *obj) const override
	Compare two TNamed objects.

void	Copy (TObject &named) const override
	Copy this to obj.

virtual void	FillBuffer (char *&buffer)
	Encode TNamed into output buffer.

const char *	GetName () const override
	Returns name of object.

const char *	GetTitle () const override
	Returns title of object.

ULong_t	Hash () const override
	Return hash value for this object.

TClass *	IsA () const override

Bool_t	IsSortable () const override

void	ls (Option_t *option="") const override
	List TNamed name and title.

TNamed &	operator= (const TNamed &rhs)
	TNamed assignment operator.

void	Print (Option_t *option="") const override
	Print TNamed name and title.

virtual void	SetName (const char *name)
	Set the name of the TNamed.

virtual void	SetNameTitle (const char name, const char title)
	Set all the TNamed parameters (name and title).

virtual void	SetTitle (const char *title="")
	Set the title of the TNamed.

virtual Int_t	Sizeof () const
	Return size of the TNamed part of the TObject.

void	Streamer (TBuffer &) override
	Stream an object of class TObject.

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

Public Member Functions inherited from TObject
	TObject ()
	TObject constructor.

	TObject (const TObject &object)
	TObject copy ctor.

virtual	~TObject ()
	TObject destructor.

void	AbstractMethod (const char *method) const
	Use this method to implement an "abstract" method that you don't want to leave purely abstract.

virtual void	AppendPad (Option_t *option="")
	Append graphics object to current pad.

virtual void	Browse (TBrowser *b)
	Browse object. May be overridden for another default action.

ULong_t	CheckedHash ()
	Check and record whether this class has a consistent Hash/RecursiveRemove setup (*) and then return the regular Hash value for this object.

virtual const char *	ClassName () const
	Returns name of class to which the object belongs.

virtual void	Delete (Option_t *option="")
	Delete this object.

virtual Int_t	DistancetoPrimitive (Int_t px, Int_t py)
	Computes distance from point (px,py) to the object.

virtual void	Draw (Option_t *option="")
	Default Draw method for all objects.

virtual void	DrawClass () const
	Draw class inheritance tree of the class to which this object belongs.

virtual TObject *	DrawClone (Option_t *option="") const
	Draw a clone of this object in the current selected pad with: `gROOT->SetSelectedPad(c1)`.

virtual void	Dump () const
	Dump contents of object on stdout.

virtual void	Error (const char method, const char msgfmt,...) const
	Issue error message.

virtual void	Execute (const char method, const char params, Int_t *error=nullptr)
	Execute method on this object with the given parameter string, e.g.

virtual void	Execute (TMethod method, TObjArray params, Int_t *error=nullptr)
	Execute method on this object with parameters stored in the TObjArray.

virtual void	ExecuteEvent (Int_t event, Int_t px, Int_t py)
	Execute action corresponding to an event at (px,py).

virtual void	Fatal (const char method, const char msgfmt,...) const
	Issue fatal error message.

virtual TObject *	FindObject (const char *name) const
	Must be redefined in derived classes.

virtual TObject *	FindObject (const TObject *obj) const
	Must be redefined in derived classes.

virtual Option_t *	GetDrawOption () const
	Get option used by the graphics system to draw this object.

virtual const char *	GetIconName () const
	Returns mime type name of object.

virtual char *	GetObjectInfo (Int_t px, Int_t py) const
	Returns string containing info about the object at position (px,py).

virtual Option_t *	GetOption () const

virtual UInt_t	GetUniqueID () const
	Return the unique object id.

virtual Bool_t	HandleTimer (TTimer *timer)
	Execute action in response of a timer timing out.

Bool_t	HasInconsistentHash () const
	Return true is the type of this object is known to have an inconsistent setup for Hash and RecursiveRemove (i.e.

virtual void	Info (const char method, const char msgfmt,...) const
	Issue info message.

virtual Bool_t	InheritsFrom (const char *classname) const
	Returns kTRUE if object inherits from class "classname".

virtual Bool_t	InheritsFrom (const TClass *cl) const
	Returns kTRUE if object inherits from TClass cl.

virtual void	Inspect () const
	Dump contents of this object in a graphics canvas.

void	InvertBit (UInt_t f)

Bool_t	IsDestructed () const
	IsDestructed.

virtual Bool_t	IsEqual (const TObject *obj) const
	Default equal comparison (objects are equal if they have the same address in memory).

virtual Bool_t	IsFolder () const
	Returns kTRUE in case object contains browsable objects (like containers or lists of other objects).

R__ALWAYS_INLINE Bool_t	IsOnHeap () const

R__ALWAYS_INLINE Bool_t	IsZombie () const

void	MayNotUse (const char *method) const
	Use this method to signal that a method (defined in a base class) may not be called in a derived class (in principle against good design since a child class should not provide less functionality than its parent, however, sometimes it is necessary).

virtual Bool_t	Notify ()
	This method must be overridden to handle object notification.

void	Obsolete (const char method, const char asOfVers, const char *removedFromVers) const
	Use this method to declare a method obsolete.

void	operator delete (void *ptr)
	Operator delete.

void	operator delete[] (void *ptr)
	Operator delete [].

void *	operator new (size_t sz)

void *	operator new (size_t sz, void *vp)

void *	operator new[] (size_t sz)

void *	operator new[] (size_t sz, void *vp)

TObject &	operator= (const TObject &rhs)
	TObject assignment operator.

virtual void	Paint (Option_t *option="")
	This method must be overridden if a class wants to paint itself.

virtual void	Pop ()
	Pop on object drawn in a pad to the top of the display list.

virtual Int_t	Read (const char *name)
	Read contents of object with specified name from the current directory.

virtual void	RecursiveRemove (TObject *obj)
	Recursively remove this object from a list.

void	ResetBit (UInt_t f)

virtual void	SaveAs (const char filename="", Option_t option="") const
	Save this object in the file specified by filename.

virtual void	SavePrimitive (std::ostream &out, Option_t *option="")
	Save a primitive as a C++ statement(s) on output stream "out".

void	SetBit (UInt_t f)

void	SetBit (UInt_t f, Bool_t set)
	Set or unset the user status bits as specified in f.

virtual void	SetDrawOption (Option_t *option="")
	Set drawing option for object.

virtual void	SetUniqueID (UInt_t uid)
	Set the unique object id.

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

virtual void	SysError (const char method, const char msgfmt,...) const
	Issue system error message.

R__ALWAYS_INLINE Bool_t	TestBit (UInt_t f) const

Int_t	TestBits (UInt_t f) const

virtual void	UseCurrentStyle ()
	Set current style settings in this object This function is called when either TCanvas::UseCurrentStyle or TROOT::ForceStyle have been invoked.

virtual void	Warning (const char method, const char msgfmt,...) const
	Issue warning message.

virtual Int_t	Write (const char *name=nullptr, Int_t option=0, Int_t bufsize=0)
	Write this object to the current directory.

virtual Int_t	Write (const char *name=nullptr, Int_t option=0, Int_t bufsize=0) const
	Write this object to the current directory.

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::IntervalCalculator
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

Static Public Member Functions inherited from TNamed
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

Static Public Member Functions inherited from TObject
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

static Longptr_t	GetDtorOnly ()
	Return destructor only flag.

static Bool_t	GetObjectStat ()
	Get status of object stat flag.

static void	SetDtorOnly (void *obj)
	Set destructor only flag.

static void	SetObjectStat (Bool_t stat)
	Turn on/off tracking of objects in the TObjectTable.

Protected Member Functions
void	ApproximatePosterior () const
	approximate posterior in nbins using a TF1 scan the poi values and evaluate the posterior at each point and save the result in a cloned TF1 For each point the posterior is evaluated by integrating the nuisance parameters

void	ClearAll () const
	clear all cached pdf objects

void	ComputeIntervalFromApproxPosterior (double c1, double c2) const
	compute the interval using the approximate posterior function

void	ComputeIntervalFromCdf (double c1, double c2) const
	internal function compute the interval using Cdf integration

void	ComputeIntervalUsingRooFit (double c1, double c2) const
	internal function compute the interval using RooFit

void	ComputeShortestInterval () const
	compute the shortest interval from the histogram representing the posterior

Protected Member Functions inherited from TObject
virtual void	DoError (int level, const char location, const char fmt, va_list va) const
	Interface to ErrorHandler (protected).

void	MakeZombie ()

Private Attributes
TF1 *	fApproxPosterior
	TF1 representing the scanned posterior function.

double	fBrfPrecision
	root finder precision

RooArgSet	fConditionalObs
	conditional observables

RooAbsData *	fData
	data set

RooArgSet	fGlobalObs
	global observables

RooAbsReal *	fIntegratedLikelihood
	integrated likelihood function, i.e - unnormalized posterior function

TString	fIntegrationType

double	fLeftSideFraction
	fraction of probability content on left side of interval

RooAbsReal *	fLikelihood
	internal pointer to likelihood function

std::unique_ptr< RooAbsReal >	fLogLike
	internal pointer to log likelihood function

double	fLower
	computer lower interval bound

double	fNLLMin
	minimum value of Nll

int	fNScanBins
	number of bins to scan, if = -1 no scan is done (default)

RooArgSet	fNuisanceParameters
	nuisance parameters

RooAbsPdf *	fNuisancePdf
	nuisance pdf (needed when using nuisance sampling technique)

int	fNumIterations
	number of iterations (when using ToyMC)

RooAbsPdf *	fPdf
	model pdf (could contain the nuisance pdf as constraint term)

RooArgSet	fPOI
	POI.

ROOT::Math::IGenFunction *	fPosteriorFunction
	function representing the posterior

RooAbsPdf *	fPosteriorPdf
	normalized (on the poi) posterior pdf

RooAbsPdf *	fPriorPdf
	prior pdf (typically for the POI)

RooAbsPdf *	fProductPdf
	internal pointer to model * prior

double	fSize
	size used for getting the interval

double	fUpper
	upper interval bound

bool	fValidInterval

Additional Inherited Members
Public Types inherited from TObject
enum	{ kIsOnHeap = 0x01000000 , kNotDeleted = 0x02000000 , kZombie = 0x04000000 , kInconsistent = 0x08000000 , kBitMask = 0x00ffffff }

enum	{ kSingleKey = (1ULL << ( 0 )) , kOverwrite = (1ULL << ( 1 )) , kWriteDelete = (1ULL << ( 2 )) }

enum	EDeprecatedStatusBits { kObjInCanvas = (1ULL << ( 3 )) }

enum	EStatusBits { kCanDelete = (1ULL << ( 0 )) , kMustCleanup = (1ULL << ( 3 )) , kIsReferenced = (1ULL << ( 4 )) , kHasUUID = (1ULL << ( 5 )) , kCannotPick = (1ULL << ( 6 )) , kNoContextMenu = (1ULL << ( 8 )) , kInvalidObject = (1ULL << ( 13 )) }

Protected Types inherited from TObject
enum	{ kOnlyPrepStep = (1ULL << ( 3 )) }

Protected Attributes inherited from TNamed
TString	fName

TString	fTitle

#include <RooStats/BayesianCalculator.h>

Inheritance diagram for RooStats::BayesianCalculator:

[legend]

Constructor & Destructor Documentation

◆ BayesianCalculator() [1/3]

RooStats::BayesianCalculator::BayesianCalculator ( )

constructor

default constructor

Definition at line 638 of file BayesianCalculator.cxx.

◆ BayesianCalculator() [2/3]

RooStats::BayesianCalculator::BayesianCalculator	(	RooAbsData &	data,
		RooAbsPdf &	pdf,
		const RooArgSet &	POI,
		RooAbsPdf &	priorPdf,
		const RooArgSet *	nuisanceParameters = `nullptr`
	)

Constructor from data set, model pdf, parameter of interests and prior pdf If nuisance parameters are given they will be integrated according either to the prior or their constraint term included in the model.

Definition at line 660 of file BayesianCalculator.cxx.

◆ BayesianCalculator() [3/3]

RooStats::BayesianCalculator::BayesianCalculator	(	RooAbsData &	data,
		ModelConfig &	model
	)

Constructor from a data set and a ModelConfig model pdf, poi and nuisances will be taken from the ModelConfig.

Definition at line 691 of file BayesianCalculator.cxx.

◆ ~BayesianCalculator()

RooStats::BayesianCalculator::~BayesianCalculator ( )

override

destructor

Definition at line 711 of file BayesianCalculator.cxx.

Member Function Documentation

◆ ApproximatePosterior()

void RooStats::BayesianCalculator::ApproximatePosterior ( ) const

protected

approximate posterior in nbins using a TF1 scan the poi values and evaluate the posterior at each point and save the result in a cloned TF1 For each point the posterior is evaluated by integrating the nuisance parameters

Definition at line 1324 of file BayesianCalculator.cxx.

◆ Class()

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

static

Returns: TClass describing this class

◆ Class_Name()

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

static

Returns: Name of this class

◆ Class_Version()

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

inlinestaticconstexpr

Returns: Version of this class

Definition at line 194 of file BayesianCalculator.h.

◆ ClearAll()

void RooStats::BayesianCalculator::ClearAll ( ) const

protected

clear all cached pdf objects

Definition at line 720 of file BayesianCalculator.cxx.

◆ ComputeIntervalFromApproxPosterior()

void RooStats::BayesianCalculator::ComputeIntervalFromApproxPosterior	(	double	c1,
		double	c2
	)		const

protected

compute the interval using the approximate posterior function

Definition at line 1369 of file BayesianCalculator.cxx.

◆ ComputeIntervalFromCdf()

void RooStats::BayesianCalculator::ComputeIntervalFromCdf	(	double	c1,
		double	c2
	)		const

protected

internal function compute the interval using Cdf integration

Definition at line 1248 of file BayesianCalculator.cxx.

◆ ComputeIntervalUsingRooFit()

void RooStats::BayesianCalculator::ComputeIntervalUsingRooFit	(	double	c1,
		double	c2
	)		const

protected

internal function compute the interval using RooFit

Definition at line 1199 of file BayesianCalculator.cxx.

◆ ComputeShortestInterval()

void RooStats::BayesianCalculator::ComputeShortestInterval ( ) const

protected

compute the shortest interval from the histogram representing the posterior

Definition at line 1390 of file BayesianCalculator.cxx.

◆ ConfidenceLevel()

double RooStats::BayesianCalculator::ConfidenceLevel ( ) const

inlineoverridevirtual

Get the Confidence level for the test.

Implements RooStats::IntervalCalculator.

Definition at line 108 of file BayesianCalculator.h.

◆ DeclFileName()

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

inlinestatic

Returns: Name of the file containing the class declaration

Definition at line 194 of file BayesianCalculator.h.

◆ ForceNuisancePdf()

void RooStats::BayesianCalculator::ForceNuisancePdf ( RooAbsPdf & pdf )

inline

Definition at line 142 of file BayesianCalculator.h.

◆ GetInterval()

SimpleInterval * RooStats::BayesianCalculator::GetInterval ( ) const

overridevirtual

compute the interval.

Compute the interval.

By Default a central interval is computed By using SetLeftTileFraction can control if central/ upper/lower interval For shortest interval use SetShortestInterval(true)

By Default a central interval is computed and the result is a SimpleInterval object.

Using the method (to be called before SetInterval) SetLeftSideTailFraction the user can choose the type of interval. By default the returned interval is a central interval with the confidence level specified previously in the constructor ( LeftSideTailFraction = 0.5).

For lower limit use SetLeftSideTailFraction = 1
For upper limit use SetLeftSideTailFraction = 0
for shortest intervals use SetLeftSideTailFraction = -1 or call the method SetShortestInterval()

NOTE: The BayesianCalculator covers only the case with one single parameter of interest

NOTE: User takes ownership of the returned object

Implements RooStats::IntervalCalculator.

Definition at line 1101 of file BayesianCalculator.cxx.

◆ GetMode()

double RooStats::BayesianCalculator::GetMode ( ) const

return the mode (most probable value of the posterior function)

Returns the value of the parameter for the point in parameter-space that is the most likely.

How do we do if there are points that are equi-probable? use approximate posterior t.b.d use real function to find the mode

Definition at line 1188 of file BayesianCalculator.cxx.

◆ GetPosteriorFunction()

RooAbsReal * RooStats::BayesianCalculator::GetPosteriorFunction ( ) const

return posterior function (object is managed by the BayesianCalculator class)

Build and return the posterior function (not normalized) as a RooAbsReal the posterior is obtained from the product of the likelihood function and the prior pdf which is then integrated in the nuisance parameters (if existing).

A prior function for the nuisance can be specified either in the prior pdf object or in the model itself. If no prior nuisance is specified, but prior parameters are then the integration is performed assuming a flat prior for the nuisance parameters.

NOTE: the return object is managed by the BayesianCalculator class, users do not need to delete it, but the object will be deleted when the BayesiabCalculator object is deleted

Definition at line 775 of file BayesianCalculator.cxx.

◆ GetPosteriorHistogram()

TH1 * RooStats::BayesianCalculator::GetPosteriorHistogram ( ) const

return the approximate posterior as histogram (TH1 object). Note the object is managed by the BayesianCalculator class

When am approximate posterior is computed binninig the parameter of interest (poi) range (see SetScanOfPosteriors) an histogram is created and can be returned to the user A nullptr is instead returned when the posterior is computed without binning the poi.

NOTE: the returned object is managed by the BayesianCalculator class, if the user wants to take ownership of the returned histogram, he needs to clone or copy the return object.

Definition at line 997 of file BayesianCalculator.cxx.

◆ GetPosteriorPdf()

RooAbsPdf * RooStats::BayesianCalculator::GetPosteriorPdf ( ) const

return posterior pdf (object is managed by the user)

Build and return the posterior pdf (i.e posterior function normalized to all range of poi) Note that an extra integration in the POI is required for the normalization NOTE: user must delete the returned object.

Definition at line 973 of file BayesianCalculator.cxx.

◆ GetPosteriorPlot()

RooPlot * RooStats::BayesianCalculator::GetPosteriorPlot	(	bool	norm = `false`,
		double	precision = `0.01`
	)		const

get the plot with option to get it normalized

return a RooPlot with the posterior and the credibility region NOTE: User takes ownership of the returned object

Definition at line 1007 of file BayesianCalculator.cxx.

◆ IsA()

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

inlineoverridevirtual

Returns: TClass describing current object

Reimplemented from RooStats::IntervalCalculator.

Definition at line 194 of file BayesianCalculator.h.

◆ SetBrfPrecision()

void RooStats::BayesianCalculator::SetBrfPrecision ( double precision )

inline

set the precision of the Root Finder

Definition at line 121 of file BayesianCalculator.h.

◆ SetConditionalObservables()

virtual void RooStats::BayesianCalculator::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

Definition at line 92 of file BayesianCalculator.h.

◆ SetConfidenceLevel()

void RooStats::BayesianCalculator::SetConfidenceLevel ( double cl )

inlineoverridevirtual

set the confidence level for the interval (eg. 0.95 for a 95% Confidence Interval)

Implements RooStats::IntervalCalculator.

Definition at line 104 of file BayesianCalculator.h.

◆ SetData()

void RooStats::BayesianCalculator::SetData ( RooAbsData & )

inlineoverridevirtual

Set the DataSet ( add to the workspace if not already there ?)

Implements RooStats::IntervalCalculator.

Definition at line 72 of file BayesianCalculator.h.

◆ SetGlobalObservables()

virtual void RooStats::BayesianCalculator::SetGlobalObservables ( const RooArgSet & set )

inlinevirtual

set the global observables which will be used when creating the NLL so the constraint pdf's will be normalized correctly on the global observables when computing the NLL

Definition at line 96 of file BayesianCalculator.h.

◆ SetIntegrationType()

void RooStats::BayesianCalculator::SetIntegrationType ( const char * type )

set the integration type (possible type are) :

1D integration ( used when only one nuisance and when the posterior is scanned): adaptive , gauss, nonadaptive
multidim:
- ADAPTIVE, adaptive numerical integration The parameter numIters (settable with SetNumIters) is the max number of function calls. It can be reduced to make the integration faster but it will be difficult to reach the required tolerance
- VEGAS MC integration method based on importance sampling - numIters is number of function calls Extra Vegas parameter can be set using IntegratorMultiDimOptions class
- MISER MC integration method based on stratified sampling See also http://en.wikipedia.org/wiki/Monte_Carlo_integration for VEGAS and MISER description
- PLAIN simple MC integration method, where the max number of calls can be specified using SetNumIters(numIters)

Extra integration types are:

TOYMC: evaluate posterior by generating toy MC for the nuisance parameters. It is a MC integration, where the function is sampled according to the nuisance. It is convenient to use when all the nuisance are uncorrelated and it is efficient to generate them The toy are generated by default for each poi values (this method has been proposed and provided by J.P Chou)
1-TOYMC : same method as before but in this case the toys are generated only one time and then used for each poi value. It can be convenient when the generation time is much larger than the evaluation time, otherwise it is recommended to re-generate the toy for each poi scanned point of the posterior function
ROOFIT: use roofit default integration methods which will produce a nested integral (not recommended for more than 1 nuisance parameters)

Definition at line 1079 of file BayesianCalculator.cxx.

◆ SetLeftSideTailFraction()

void RooStats::BayesianCalculator::SetLeftSideTailFraction ( double leftSideFraction )

inline

set the fraction of probability content on the left tail Central limits use 0.5 (default case) for upper limits it is 0 and 1 for lower limit For shortest intervals a negative value (i.e.

-1) must be given

Definition at line 114 of file BayesianCalculator.h.

◆ SetModel()

void RooStats::BayesianCalculator::SetModel ( const ModelConfig & model )

overridevirtual

◆ Size()

double RooStats::BayesianCalculator::Size ( ) const

inlineoverridevirtual

Get the size of the test (eg. rate of Type I error)

Implements RooStats::IntervalCalculator.

Definition at line 106 of file BayesianCalculator.h.

◆ Streamer()

bool RooStats::BayesianCalculator::fValidInterval

mutableprivate

Definition at line 188 of file BayesianCalculator.h.

Libraries for RooStats::BayesianCalculator:

[legend]

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

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

Public Member Functions

Static Public Member Functions

Protected Member Functions

Private Attributes

Additional Inherited Members

Constructor & Destructor Documentation

◆ BayesianCalculator() [1/3]

◆ BayesianCalculator() [2/3]

◆ BayesianCalculator() [3/3]

◆ ~BayesianCalculator()

Member Function Documentation

◆ ApproximatePosterior()

◆ Class()

◆ Class_Name()

◆ Class_Version()

◆ ClearAll()

◆ ComputeIntervalFromApproxPosterior()

◆ ComputeIntervalFromCdf()

◆ ComputeIntervalUsingRooFit()

◆ ComputeShortestInterval()

◆ ConfidenceLevel()

◆ DeclFileName()

◆ ForceNuisancePdf()

◆ GetInterval()

◆ GetMode()

◆ GetPosteriorFunction()

◆ GetPosteriorHistogram()

◆ GetPosteriorPdf()

◆ GetPosteriorPlot()

◆ IsA()

◆ SetBrfPrecision()

◆ SetConditionalObservables()

◆ SetConfidenceLevel()

◆ SetData()

◆ SetGlobalObservables()

◆ SetIntegrationType()

◆ SetLeftSideTailFraction()

◆ SetModel()

◆ SetNuisanceParameters()

◆ SetNumIters()

◆ SetParameters()

◆ SetPriorPdf()

◆ SetScanOfPosterior()

◆ SetShortestInterval()

◆ SetTestSize()

◆ Size()

◆ Streamer()

◆ StreamerNVirtual()

Member Data Documentation

◆ fApproxPosterior

◆ fBrfPrecision

◆ fConditionalObs

◆ fData

◆ fGlobalObs

◆ fIntegratedLikelihood

◆ fIntegrationType

◆ fLeftSideFraction

◆ fLikelihood

◆ fLogLike

◆ fLower

◆ fNLLMin

◆ fNScanBins

◆ fNuisanceParameters

◆ fNuisancePdf

◆ fNumIterations

◆ fPdf

◆ fPOI

◆ fPosteriorFunction

◆ fPosteriorPdf

◆ fPriorPdf

◆ fProductPdf

◆ fSize

◆ fUpper

◆ fValidInterval