ROOT » MATH » PHYSICS » TRolke

class TRolke: public TObject


  TRolke

  This class computes confidence intervals for the rate of a Poisson
  process in the presence of uncertain background and/or efficiency.

  The treatment and the resulting limits are fully frequentist. The
  limit calculations make use of the profile likelihood method.

      Author: Jan Conrad (CERN) 2004
      Updated: Johan Lundberg (CERN) 2009

      Copyright CERN 2004,2009           Jan.Conrad@cern.ch,
                                     Johan.Lundberg@cern.ch

  For a full list of methods and their syntax, and build instructions,
  consult the header file TRolke.h.


  Examples/tutorials are found in the separate file Rolke.C





  TRolke implements the following Models


  The signal is always assumed to be Poisson, with the following
  combinations of models of background and detection efficiency:

  If unsure, first consider model 3, 4 or 5.

       1: SetPoissonBkgBinomEff(x,y,z,tau,m)

          Background: Poisson
          Efficiency: Binomial

          when the background is simultaneously measured
          from sidebands (or MC), and
          the signal efficiency was determined from Monte Carlo

       2: SetPoissonBkgGaussEff(x,y,em,sde,tau)

          Background: Poisson
          Efficiency: Gaussian

          when the background is simultaneously measured
          from sidebands (or MC), and
          the efficiency is modeled as Gaussian

       3: SetGaussBkgGaussEff(x,bm,em,sde,sdb)

          Background: Gaussian
          Efficiency: Gaussian

          when background and efficiency can both be
          modeled as Gaussian.

       4: SetPoissonBkgKnownEff(x,y,tau,e)

          Background: Poisson
          Efficiency: Known

          when the background is simultaneously measured
          from sidebands (or MC).

       5: SetGaussBkgKnownEff(x,bm,sdb,e)

          Background: Gaussian
          Efficiency: Known

          when background is Gaussian

       6: SetKnownBkgBinomEff(x,z,b,m)

          Background: Known
          Efficiency: Binomial

          when signal efficiency was determined from Monte Carlo

       7: SetKnownBkgGaussEff(x,em,sde,b)

          Background: Known
          Efficiency: Gaussian

          when background is known and efficiency Gaussian

  Parameters and further explanation


  For all models:


    x = number of observed events in the experiment

    Efficiency (e or em) is the detection probability for signal.
    A low efficiency hence generally means weaker limits.
    If the efficiency of an experiment (with analysis cuts) is
    dealt with elsewhere, em or e can be set to one.

  For Poisson background measurements (sideband or MC):


    y = number of observed events in background region
    tau =
        Either: the ratio between signal and background region
        in case background is observed.
        Or: the ratio between observed and simulated live-time
        in case background is determined from MC.

  For Gaussian efficiency or background:


    bm  = estimate of the background
    sdb = corresponding standard deviation

    em  = estimate of the efficiency
    sde = corresponding standard deviation

        If the efficiency scale of dealt with elsewhere,
        set em to 1 and sde to the relative uncertainty.

  For Binomial signal efficiency:


     m = number of MC events generated
     z = number of MC events observed

  For the case of known background expectation or known efficiency:


     e = true efficiency (considered known)
     b = background expectation value (considered known)





  The confidence level (CL) is set either at construction
  time or with either of SetCL or SetCLSigmas

  The TRolke method is very similar to the one used in MINUIT (MINOS).

  Two options are offered to deal with cases where the maximum likelihood
  estimate (MLE) is not in the physical region. Version "bounded likelihood"
  is the one used by MINOS if bounds for the physical region are chosen.
  Unbounded likelihood (the default) allows the MLE to be in the
  unphysical region. It has however better coverage.
  For more details consult the reference (see below).

  For a description of the method and its properties:

  W.Rolke, A. Lopez, J. Conrad and Fred James
  "Limits and Confidence Intervals in presence of nuisance parameters"
   http://lanl.arxiv.org/abs/physics/0403059
   Nucl.Instrum.Meth.A551:493-503,2005

  Should I use TRolke, TFeldmanCousins, TLimit?


  1. Does TRolke make TFeldmanCousins obsolete?

  Certainly not. TFeldmanCousins is the fully frequentist construction and
  should be used in case of no (or negligible) uncertainties. It is however
  not capable of treating uncertainties in nuisance parameters. In other
  words, it does not handle background expectations or signal efficiencies
  which are known only with some limited accuracy.

  TRolke is designed for this case and it is shown in the reference above
  that it has good coverage properties for most cases, and can be used
  where FeldmannCousins can't.

  2. What are the advantages of TRolke over TLimit?

  TRolke is fully frequentist. TLimit treats nuisance parameters Bayesian.

  For a coverage study of a Bayesian method refer to
  physics/0408039 (Tegenfeldt & J.C). However, this note studies
  the coverage of Feldman&Cousins with Bayesian treatment of nuisance
  parameters. To make a long story short: using the Bayesian method you
  might introduce a small amount of over-coverage (though I haven't shown it
  for TLimit). On the other hand, coverage of course is a not so interesting
  when you consider yourself a Bayesian.


Function Members (Methods)

public:
virtual~TRolke()
voidTObject::AbstractMethod(const char* method) const
virtual voidTObject::AppendPad(Option_t* option = "")
virtual voidTObject::Browse(TBrowser* b)
Double_tCalculateInterval(Int_t x, Int_t y, Int_t z, Double_t bm, Double_t em, Double_t e, Int_t mid, Double_t sde, Double_t sdb, Double_t tau, Double_t b, Int_t m)
static TClass*Class()
virtual const char*TObject::ClassName() const
virtual voidTObject::Clear(Option_t* = "")
virtual TObject*TObject::Clone(const char* newname = "") const
virtual Int_tTObject::Compare(const TObject* obj) const
virtual voidTObject::Copy(TObject& object) const
virtual voidTObject::Delete(Option_t* option = "")MENU
virtual Int_tTObject::DistancetoPrimitive(Int_t px, Int_t py)
virtual voidTObject::Draw(Option_t* option = "")
virtual voidTObject::DrawClass() constMENU
virtual TObject*TObject::DrawClone(Option_t* option = "") constMENU
virtual voidTObject::Dump() constMENU
virtual voidTObject::Error(const char* method, const char* msgfmt) const
virtual voidTObject::Execute(const char* method, const char* params, Int_t* error = 0)
virtual voidTObject::Execute(TMethod* method, TObjArray* params, Int_t* error = 0)
virtual voidTObject::ExecuteEvent(Int_t event, Int_t px, Int_t py)
virtual voidTObject::Fatal(const char* method, const char* msgfmt) const
virtual TObject*TObject::FindObject(const char* name) const
virtual TObject*TObject::FindObject(const TObject* obj) const
boolGetBounding() const
Double_tGetCL() const
boolGetCriticalNumber(Int_t& ncrit, Int_t maxtry = -1)
virtual Option_t*TObject::GetDrawOption() const
static Long_tTObject::GetDtorOnly()
virtual const char*TObject::GetIconName() const
boolGetLimits(Double_t& low, Double_t& high)
boolGetLimitsML(Double_t& low, Double_t& high, Int_t& out_x)
boolGetLimitsQuantile(Double_t& low, Double_t& high, Int_t& out_x, Double_t integral = 0.5)
Double_tGetLowerLimit()
virtual const char*TObject::GetName() const
virtual char*TObject::GetObjectInfo(Int_t px, Int_t py) const
static Bool_tTObject::GetObjectStat()
virtual Option_t*TObject::GetOption() const
boolGetSensitivity(Double_t& low, Double_t& high, Double_t pPrecision = 1.0000000000000001E-5)
virtual const char*TObject::GetTitle() const
virtual UInt_tTObject::GetUniqueID() const
Double_tGetUpperLimit()
virtual Bool_tTObject::HandleTimer(TTimer* timer)
virtual ULong_tTObject::Hash() const
virtual voidTObject::Info(const char* method, const char* msgfmt) const
virtual Bool_tTObject::InheritsFrom(const char* classname) const
virtual Bool_tTObject::InheritsFrom(const TClass* cl) const
virtual voidTObject::Inspect() constMENU
voidTObject::InvertBit(UInt_t f)
virtual TClass*IsA() const
virtual Bool_tTObject::IsEqual(const TObject* obj) const
virtual Bool_tTObject::IsFolder() const
Bool_tTObject::IsOnHeap() const
virtual Bool_tTObject::IsSortable() const
Bool_tTObject::IsZombie() const
virtual voidTObject::ls(Option_t* option = "") const
voidTObject::MayNotUse(const char* method) const
virtual Bool_tTObject::Notify()
voidTObject::Obsolete(const char* method, const char* asOfVers, const char* removedFromVers) const
voidTObject::operator delete(void* ptr)
voidTObject::operator delete(void* ptr, void* vp)
voidTObject::operator delete[](void* ptr)
voidTObject::operator delete[](void* ptr, void* vp)
void*TObject::operator new(size_t sz)
void*TObject::operator new(size_t sz, void* vp)
void*TObject::operator new[](size_t sz)
void*TObject::operator new[](size_t sz, void* vp)
TRolke&operator=(const TRolke&)
virtual voidTObject::Paint(Option_t* option = "")
virtual voidTObject::Pop()
virtual voidPrint(Option_t*) const
virtual Int_tTObject::Read(const char* name)
virtual voidTObject::RecursiveRemove(TObject* obj)
voidTObject::ResetBit(UInt_t f)
virtual voidTObject::SaveAs(const char* filename = "", Option_t* option = "") constMENU
virtual voidTObject::SavePrimitive(ostream& out, Option_t* option = "")
voidTObject::SetBit(UInt_t f)
voidTObject::SetBit(UInt_t f, Bool_t set)
voidSetBounding(const bool bnd)
voidSetCL(Double_t CL)
voidSetCLSigmas(Double_t CLsigmas)
virtual voidTObject::SetDrawOption(Option_t* option = "")MENU
static voidTObject::SetDtorOnly(void* obj)
voidSetGaussBkgGaussEff(Int_t x, Double_t bm, Double_t em, Double_t sde, Double_t sdb)
voidSetGaussBkgKnownEff(Int_t x, Double_t bm, Double_t sdb, Double_t e)
voidSetKnownBkgBinomEff(Int_t x, Int_t z, Int_t m, Double_t b)
voidSetKnownBkgGaussEff(Int_t x, Double_t em, Double_t sde, Double_t b)
static voidTObject::SetObjectStat(Bool_t stat)
voidSetPoissonBkgBinomEff(Int_t x, Int_t y, Int_t z, Double_t tau, Int_t m)
voidSetPoissonBkgGaussEff(Int_t x, Int_t y, Double_t em, Double_t tau, Double_t sde)
voidSetPoissonBkgKnownEff(Int_t x, Int_t y, Double_t tau, Double_t e)
voidSetSwitch(bool bnd)
virtual voidTObject::SetUniqueID(UInt_t uid)
virtual voidShowMembers(TMemberInspector& insp) const
virtual voidStreamer(TBuffer&)
voidStreamerNVirtual(TBuffer& ClassDef_StreamerNVirtual_b)
virtual voidTObject::SysError(const char* method, const char* msgfmt) const
Bool_tTObject::TestBit(UInt_t f) const
Int_tTObject::TestBits(UInt_t f) const
TRolke(const TRolke&)
TRolke(Double_t CL = 0.90000000000000002, Option_t* option = "")
virtual voidTObject::UseCurrentStyle()
virtual voidTObject::Warning(const char* method, const char* msgfmt) const
virtual Int_tTObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0)
virtual Int_tTObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0) const
protected:
virtual voidTObject::DoError(int level, const char* location, const char* fmt, va_list va) const
voidTObject::MakeZombie()
private:
Double_tComputeInterval(Int_t x, Int_t y, Int_t z, Double_t bm, Double_t em, Double_t e, Int_t mid, Double_t sde, Double_t sdb, Double_t tau, Double_t b, Int_t m)
Double_tEvalLikeMod1(Double_t mu, Int_t x, Int_t y, Int_t z, Double_t tau, Int_t m, Int_t what)
Double_tEvalLikeMod2(Double_t mu, Int_t x, Int_t y, Double_t em, Double_t sde, Double_t tau, Int_t what)
Double_tEvalLikeMod3(Double_t mu, Int_t x, Double_t bm, Double_t em, Double_t sde, Double_t sdb, Int_t what)
Double_tEvalLikeMod4(Double_t mu, Int_t x, Int_t y, Double_t tau, Int_t what)
Double_tEvalLikeMod5(Double_t mu, Int_t x, Double_t bm, Double_t sdb, Int_t what)
Double_tEvalLikeMod6(Double_t mu, Int_t x, Int_t z, Double_t b, Int_t m, Int_t what)
Double_tEvalLikeMod7(Double_t mu, Int_t x, Double_t em, Double_t sde, Double_t b, Int_t what)
static Double_tEvalMonomial(Double_t x, const Int_t[] coef, Int_t N)
static Double_tEvalPolynomial(Double_t x, const Int_t[] coef, Int_t N)
Double_tGetBackground()
Double_tInterval(Int_t x, Int_t y, Int_t z, Double_t bm, Double_t em, Double_t e, Int_t mid, Double_t sde, Double_t sdb, Double_t tau, Double_t b, Int_t m)
Double_tLikeGradMod1(Double_t e, Double_t mu, Int_t x, Int_t y, Int_t z, Double_t tau, Int_t m)
Double_tLikelihood(Double_t mu, Int_t x, Int_t y, Int_t z, Double_t bm, Double_t em, Int_t mid, Double_t sde, Double_t sdb, Double_t tau, Double_t b, Int_t m, Int_t what)
Double_tLikeMod1(Double_t mu, Double_t b, Double_t e, Int_t x, Int_t y, Int_t z, Double_t tau, Int_t m)
Double_tLikeMod2(Double_t mu, Double_t b, Double_t e, Int_t x, Int_t y, Double_t em, Double_t tau, Double_t v)
Double_tLikeMod3(Double_t mu, Double_t b, Double_t e, Int_t x, Double_t bm, Double_t em, Double_t u, Double_t v)
Double_tLikeMod4(Double_t mu, Double_t b, Int_t x, Int_t y, Double_t tau)
Double_tLikeMod5(Double_t mu, Double_t b, Int_t x, Double_t bm, Double_t u)
Double_tLikeMod6(Double_t mu, Double_t b, Double_t e, Int_t x, Int_t z, Int_t m)
Double_tLikeMod7(Double_t mu, Double_t b, Double_t e, Int_t x, Double_t em, Double_t v)
Double_tLogFactorial(Int_t n)
voidProfLikeMod1(Double_t mu, Double_t& b, Double_t& e, Int_t x, Int_t y, Int_t z, Double_t tau, Int_t m)
voidSetModelParameters()
voidSetModelParameters(Int_t x, Int_t y, Int_t z, Double_t bm, Double_t em, Double_t e, Int_t mid, Double_t sde, Double_t sdb, Double_t tau, Double_t b, Int_t m)

Data Members

public:
static TObject::<anonymous>TObject::kBitMask
static TObject::EStatusBitsTObject::kCanDelete
static TObject::EStatusBitsTObject::kCannotPick
static TObject::EStatusBitsTObject::kHasUUID
static TObject::EStatusBitsTObject::kInvalidObject
static TObject::<anonymous>TObject::kIsOnHeap
static TObject::EStatusBitsTObject::kIsReferenced
static TObject::EStatusBitsTObject::kMustCleanup
static TObject::EStatusBitsTObject::kNoContextMenu
static TObject::<anonymous>TObject::kNotDeleted
static TObject::EStatusBitsTObject::kObjInCanvas
static TObject::<anonymous>TObject::kOverwrite
static TObject::<anonymous>TObject::kSingleKey
static TObject::<anonymous>TObject::kWriteDelete
static TObject::<anonymous>TObject::kZombie
private:
boolfBoundingfalse for unbounded likelihood
Double_tfCLconfidence level as a fraction [0.9 for 90% ]
Double_tfLowerLimitthe calculated lower limit
Int_tfNumWarningsDeprecated1
Int_tfNumWarningsDeprecated2
Double_tfUpperLimitthe calculated upper limit
Double_tf_b
Double_tf_bm
Double_tf_e
Double_tf_em
Int_tf_m
Int_tf_mid
Double_tf_sdb
Double_tf_sde
Double_tf_tau
Int_tf_x
Int_tf_y
Int_tf_z

Class Charts

Inheritance Inherited Members Includes Libraries
Class Charts

Function documentation

TRolke(Double_t CL = 0.90000000000000002, Option_t* option = "")
constructor with optional Confidence Level argument.
'option' is not used.
~TRolke()
void SetPoissonBkgBinomEff(Int_t x, Int_t y, Int_t z, Double_t tau, Int_t m)
 Model 1: Background - Poisson, Efficiency - Binomial
    x   : number of observed events in the experiment
    y   : number of observed events in background region
    z   : number of MC events observed
    tau : ratio parameter (read TRolke.cxx for details)
    m   : number of MC events generated
void SetPoissonBkgGaussEff(Int_t x, Int_t y, Double_t em, Double_t tau, Double_t sde)
 Model 2: Background - Poisson, Efficiency - Gaussian
    x   : number of observed events in the experiment
    y   : number of observed events in background region
    em  : estimate of the efficiency
    tau : ratio parameter (read TRolke.cxx for details)
    sde : efficiency estimate's standard deviation
void SetGaussBkgGaussEff(Int_t x, Double_t bm, Double_t em, Double_t sde, Double_t sdb)
 Model 3: Background - Gaussian, Efficiency - Gaussian (x,bm,em,sde,sdb)
    x   : number of observed events in the experiment
    bm  : estimate of the background
    em  : estimate of the efficiency
    sde : efficiency estimate's standard deviation
    sdb : background estimate's standard deviation
void SetPoissonBkgKnownEff(Int_t x, Int_t y, Double_t tau, Double_t e)
 Model 4: Background - Poisson, Efficiency - known     (x,y,tau,e)
    x   : number of observed events in the experiment
    y   : number of observed events in background region
    tau : ratio parameter (read TRolke.cxx for details)
    e   : true efficiency (considered known)
void SetGaussBkgKnownEff(Int_t x, Double_t bm, Double_t sdb, Double_t e)
 Model 5: Background - Gaussian, Efficiency - known    (x,bm,sdb,e
    x   : number of observed events in the experiment
    bm  : estimate of the background
    sdb : background estimate's standard deviation
    e   : true efficiency (considered known)
void SetKnownBkgBinomEff(Int_t x, Int_t z, Int_t m, Double_t b)
 Model 6: Background - known, Efficiency - Binomial    (x,z,m,b)
    x   : number of observed events in the experiment
    z   : number of MC events observed
    m   : number of MC events generated
    b   : background expectation value (considered known)
void SetKnownBkgGaussEff(Int_t x, Double_t em, Double_t sde, Double_t b)
 Model 7: Background - known, Efficiency - Gaussian    (x,em,sde,b)
    x   : number of observed events in the experiment
    em  : estimate of the efficiency
    sde : efficiency estimate's standard deviation
    b   : background expectation value (considered known)
bool GetLimits(Double_t& low, Double_t& high)
Calculate and get the upper and lower limits for the pre-specified model
Double_t GetUpperLimit()
Calculate and get upper limit for the pre-specified model
Double_t GetLowerLimit()
Calculate and get lower limit for the pre-specified model
Double_t GetBackground()
Return a simple background value (estimate/truth) given the pre-specified model
bool GetSensitivity(Double_t& low, Double_t& high, Double_t pPrecision = 1.0000000000000001E-5)
 get the upper and lower average limits based on the specified model.
 No uncertainties are considered for the Poisson weights in the averaging sum
bool GetLimitsQuantile(Double_t& low, Double_t& high, Int_t& out_x, Double_t integral = 0.5)
 get the upper and lower limits for the outcome corresponding to
   a given quantile.
   For integral=0.5 this gives the median limits
   in repeated experiments. The returned out_x is the corresponding
   (e.g. median) value of x.
   No uncertainties are considered for the Poisson weights when calculating
the Poisson integral 
bool GetLimitsML(Double_t& low, Double_t& high, Int_t& out_x)
 get the upper and lower limits for the most likely outcome.
   The returned out_x is the corresponding value of x
No uncertainties are considered for the Poisson weights when finding ML 
bool GetCriticalNumber(Int_t& ncrit, Int_t maxtry = -1)
 get the value of x corresponding to rejection of the null hypothesis.
 This means a lower limit >0 with the pre-specified Confidence Level.
 Optionally give maxtry; the maximum value of x to try. Of not, or if
 maxtry<0 an automatic mode is used.
void SetSwitch(bool bnd)
Deprecated name for SetBounding.
void Print(Option_t* ) const
Dump internals. Print members.
Double_t CalculateInterval(Int_t x, Int_t y, Int_t z, Double_t bm, Double_t em, Double_t e, Int_t mid, Double_t sde, Double_t sdb, Double_t tau, Double_t b, Int_t m)
 Deprecated and error prone model selection interface.
   It's use is trongly discouraged. 'mid' is the model ID (1 to 7).
This method is provided for backwards compatibility/developer use only. 
    x   : number of observed events in the experiment
    y   : number of observed events in background region
    z   : number of MC events observed
    bm  : estimate of the background
    em  : estimate of the efficiency
    e   : true efficiency (considered known)
    mid : internal model id (really, you should not use this method at all)
    sde : efficiency estimate's standard deviation
    sdb : background estimate's standard deviation
    tau : ratio parameter (read TRolke.cxx for details)
    b   : background expectation value (considered known)
    m   : number of MC events generated
void SetModelParameters(Int_t x, Int_t y, Int_t z, Double_t bm, Double_t em, Double_t e, Int_t mid, Double_t sde, Double_t sdb, Double_t tau, Double_t b, Int_t m)
    x   : number of observed events in the experiment
    y   : number of observed events in background region
    z   : number of MC events observed
    bm  : estimate of the background
    em  : estimate of the efficiency
    e   : true efficiency (considered known)
    mid : internal model id
    sde : efficiency estimate's standard deviation
    sdb : background estimate's standard deviation
    tau : ratio parameter (read TRolke.cxx for details)
    b   : background expectation value (considered known)
    m   : number of MC events generated
void SetModelParameters()
Clear internal model
Double_t ComputeInterval(Int_t x, Int_t y, Int_t z, Double_t bm, Double_t em, Double_t e, Int_t mid, Double_t sde, Double_t sdb, Double_t tau, Double_t b, Int_t m)
 ComputeInterval, the internals.
    x   : number of observed events in the experiment
    y   : number of observed events in background region
    z   : number of MC events observed
    bm  : estimate of the background
    em  : estimate of the efficiency
    e   : true efficiency (considered known)
    mid : internal model id (really, you should not use this method at all)
    sde : efficiency estimate's standard deviation
    sdb : background estimate's standard deviation
    tau : ratio parameter (read TRolke.cxx for details)
    b   : background expectation value (considered known)
    m   : number of MC events generated
Double_t Interval(Int_t x, Int_t y, Int_t z, Double_t bm, Double_t em, Double_t e, Int_t mid, Double_t sde, Double_t sdb, Double_t tau, Double_t b, Int_t m)
 Internal helper function 'Interval'

    x   : number of observed events in the experiment
    y   : number of observed events in background region
    z   : number of MC events observed
    bm  : estimate of the background
    em  : estimate of the efficiency
    e   : true efficiency (considered known)
    mid : internal model id (really, you should not use this method at all)
    sde : efficiency estimate's standard deviation
    sdb : background estimate's standard deviation
    tau : ratio parameter (read TRolke.cxx for details)
    b   : background expectation value (considered known)
    m   : number of MC events generated
Double_t Likelihood(Double_t mu, Int_t x, Int_t y, Int_t z, Double_t bm, Double_t em, Int_t mid, Double_t sde, Double_t sdb, Double_t tau, Double_t b, Int_t m, Int_t what)
Internal helper function
 Chooses between the different profile likelihood functions to use for the
 different models.
 Returns evaluation of the profile likelihood functions.
Double_t EvalLikeMod1(Double_t mu, Int_t x, Int_t y, Int_t z, Double_t tau, Int_t m, Int_t what)

 Calculates the Profile Likelihood for MODEL 1:
  Poisson background/ Binomial Efficiency
 what = 1: Maximum likelihood estimate is returned
 what = 2: Profile Likelihood of Maximum Likelihood estimate is returned.
 what = 3: Profile Likelihood of Test hypothesis is returned
 otherwise parameters as described in the beginning of the class)
Double_t LikeMod1(Double_t mu, Double_t b, Double_t e, Int_t x, Int_t y, Int_t z, Double_t tau, Int_t m)
 Profile Likelihood function for MODEL 1:
 Poisson background/ Binomial Efficiency
void ProfLikeMod1(Double_t mu, Double_t& b, Double_t& e, Int_t x, Int_t y, Int_t z, Double_t tau, Int_t m)
 Helper for calculation of estimates of efficiency and background for model 1

Double_t LikeGradMod1(Double_t e, Double_t mu, Int_t x, Int_t y, Int_t z, Double_t tau, Int_t m)
gradient model likelihood
Double_t EvalLikeMod2(Double_t mu, Int_t x, Int_t y, Double_t em, Double_t sde, Double_t tau, Int_t what)
 Calculates the Profile Likelihood for MODEL 2:
  Poisson background/ Gauss Efficiency
 what = 1: Maximum likelihood estimate is returned
 what = 2: Profile Likelihood of Maximum Likelihood estimate is returned.
 what = 3: Profile Likelihood of Test hypothesis is returned
 otherwise parameters as described in the beginning of the class)
Double_t LikeMod2(Double_t mu, Double_t b, Double_t e, Int_t x, Int_t y, Double_t em, Double_t tau, Double_t v)
 Profile Likelihood function for MODEL 2:
 Poisson background/Gauss Efficiency
Double_t EvalLikeMod3(Double_t mu, Int_t x, Double_t bm, Double_t em, Double_t sde, Double_t sdb, Int_t what)
 Calculates the Profile Likelihood for MODEL 3:
 Gauss  background/ Gauss Efficiency
 what = 1: Maximum likelihood estimate is returned
 what = 2: Profile Likelihood of Maximum Likelihood estimate is returned.
 what = 3: Profile Likelihood of Test hypothesis is returned
 otherwise parameters as described in the beginning of the class)
Double_t LikeMod3(Double_t mu, Double_t b, Double_t e, Int_t x, Double_t bm, Double_t em, Double_t u, Double_t v)
 Profile Likelihood function for MODEL 3:
 Gauss background/Gauss Efficiency
Double_t EvalLikeMod4(Double_t mu, Int_t x, Int_t y, Double_t tau, Int_t what)
 Calculates the Profile Likelihood for MODEL 4:
 Poiss  background/Efficiency known
 what = 1: Maximum likelihood estimate is returned
 what = 2: Profile Likelihood of Maximum Likelihood estimate is returned.
 what = 3: Profile Likelihood of Test hypothesis is returned
 otherwise parameters as described in the beginning of the class)
Double_t LikeMod4(Double_t mu, Double_t b, Int_t x, Int_t y, Double_t tau)
 Profile Likelihood function for MODEL 4:
 Poiss background/Efficiency known
Double_t EvalLikeMod5(Double_t mu, Int_t x, Double_t bm, Double_t sdb, Int_t what)
 Calculates the Profile Likelihood for MODEL 5:
 Gauss  background/Efficiency known
 what = 1: Maximum likelihood estimate is returned
 what = 2: Profile Likelihood of Maximum Likelihood estimate is returned.
 what = 3: Profile Likelihood of Test hypothesis is returned
 otherwise parameters as described in the beginning of the class)
Double_t LikeMod5(Double_t mu, Double_t b, Int_t x, Double_t bm, Double_t u)
 Profile Likelihood function for MODEL 5:
 Gauss background/Efficiency known
Double_t EvalLikeMod6(Double_t mu, Int_t x, Int_t z, Double_t b, Int_t m, Int_t what)
 Calculates the Profile Likelihood for MODEL 6:
 Background known/Efficiency binomial
 what = 1: Maximum likelihood estimate is returned
 what = 2: Profile Likelihood of Maximum Likelihood estimate is returned.
 what = 3: Profile Likelihood of Test hypothesis is returned
 otherwise parameters as described in the beginning of the class)
Double_t LikeMod6(Double_t mu, Double_t b, Double_t e, Int_t x, Int_t z, Int_t m)
 Profile Likelihood function for MODEL 6:
 background known/ Efficiency binomial
Double_t EvalLikeMod7(Double_t mu, Int_t x, Double_t em, Double_t sde, Double_t b, Int_t what)
 Calculates the Profile Likelihood for MODEL 7:
 background known/Efficiency Gauss
 what = 1: Maximum likelihood estimate is returned
 what = 2: Profile Likelihood of Maximum Likelihood estimate is returned.
 what = 3: Profile Likelihood of Test hypothesis is returned
 otherwise parameters as described in the beginning of the class)
Double_t LikeMod7(Double_t mu, Double_t b, Double_t e, Int_t x, Double_t em, Double_t v)
 Profile Likelihood function for MODEL 6:
 background known/ Efficiency gaussian
Double_t EvalPolynomial(Double_t x, const Int_t[] coef, Int_t N)
 evaluate polynomial
Double_t EvalMonomial(Double_t x, const Int_t[] coef, Int_t N)
 evaluate mononomial
Double_t LogFactorial(Int_t n)
 LogFactorial function (use the logGamma function via the relation Gamma(n+1) = n!
TRolke(Double_t CL = 0.90000000000000002, Option_t* option = "")
Constructor
Double_t GetCL() const
Get and set the Confidence Level
void SetCL(Double_t CL)
void SetCLSigmas(Double_t CLsigmas)
Set the Confidence Level in terms of Sigmas.
bool GetBounding() const
 Get the bounding mode flag. True activates bounded mode. Read
TRolke.cxx and the references therein for details. 
void SetBounding(const bool bnd)
 Get the bounding mode flag. True activates bounded mode. Read
TRolke.cxx and the references therein for details.