class RooStats::MCMCCalculator: public RooStats::IntervalCalculator, public TNamed

 default constructor

 Constructor for automatic configuration with basic settings.  Uses a
 UniformProposal,10,000 iterations, 40 burn in steps, 50 bins for each
 RooRealVar, determines interval by keys, and turns on sparse histogram
 mode in the MCMCInterval.  Finds a 95% confidence interval.

 same constructor as before but not passing a prior pdf

 Constructor for automatic configuration with basic settings.  Uses a
 UniformProposal,10,000 iterations, 40 burn in steps, 50 bins for each
 RooRealVar, determines interval by keys, and turns on sparse histogram
 mode in the MCMCInterval.  Finds a 95% confidence interval.

 alternate constructor, specifying many arguments

 set the model

 Setting automatic configuration with basic settings.  Uses a
 UniformProposal,10,000 iterations, 40 burn in steps, 50 bins for each
 RooRealVar, determines interval by keys, and turns on sparse histogram
 mode in the MCMCInterval.  Finds a 95% confidence interval.

 Main interface to get a RooStats::ConfInterval.

 default constructor

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

 Get the Confidence level for the test

 Set the DataSet if not already there

 Set the Pdf if not already there

 Set the Prior Pdf if not already there

 specify the parameters of interest in the interval

 specify the nuisance parameters (eg. the rest of the parameters)

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

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

 set the proposal function for suggesting new points for the MCMC

 set the number of iterations to run the metropolis algorithm

 set the number of steps in the chain to discard as burn-in,
 starting from the first

 set the number of bins to create for each axis when constructing the interval

 set which variables to put on each axis

 set whether to use kernel estimation to determine the interval

 set whether to use sparse histogram (if using histogram at all)