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class PoissonErrorDistribution( ErrorDistribution )	Source

To calculate a Poisson likelihood.

For one observation with n counts it holds

  f( n,x ) = xⁿ / ( e^x * n! )

where x is the expected counts

The function is mostly used to calculate the likelihood L, or easier to use log likelihood, logL.

     logL = ∑( n * log( x ) - x - log( n! ) )

Weights are not accepted in this ErrorDistribution; they are silently ignored.

Author Do Kester.

PoissonErrorDistribution( copy=None )

Constructor.

Parameters

• copy : PoissonErrorDistribution
       distribution to be copied.

copy( )

Return copy of this.

acceptWeight( )

True if the distribution accepts weights. Always false for this distribution.

getScale( problem, allpars=None )

Return the noise scale.

*** Gaussian approximation ***

Parameters

• problem : Problem
       to be solved
• allpars : array_like
       None take parameters from problem.model
       list of all parameters in the problem

logLikelihood_alt( problem, allpars )

Return the log( likelihood ) for a Poisson distribution.

Parameters

• problem : Problem
       to be solved
• allpars : array_like
       list of all parameters in the problem

logLdata( problem, allpars, mockdata=None )

Return the log( likelihood ) for each residual

logL = sum( logLdata )

Parameters

• problem : Problem
       to be solved
• allpars : array_like
       list of all parameters in the problem
• mockdata : array_like
       as calculated by the model

partialLogL_alt( problem, allpars, fitIndex )

Return the partial derivative of log( likelihood ) to the parameters.

Parameters

• problem : Problem
       to be solved
• allpars : array_like
       list of all parameters in the problem
• fitIndex : array_like
       indices of parameters to be fitted

nextPartialData( problem, allpars, fitIndex, mockdata=None )

Return the partial derivative of log( likelihood ) to the parameters.

Parameters

• problem : Problem
       to be solved
• allpars : array_like
       list of all parameters in the problem
• fitIndex : array_like
       indices of parameters to be fitted
• mockdata : array_like
       as calculated by the model

Methods inherited from ErrorDistribution

• getGaussianScale( problem, allpars=None )
• getResiduals( problem, allpars=None )
• getChisq( problem, allpars=None )
• toSigma( scale )
• isBound( )
• keepFixed( fixed=None )
• setPriors( priors )
• setLimits( limits )
• domain2Unit( dval, ks )
• unit2Domain( uval, ks )
• logCLhood( problem, allpars )
• logLhood( problem, allpars )
• partialLogL( problem, allpars, fitIndex )
• numPartialLogL( problem, allpars, fitIndex )
• updateLogL( problem, allpars, parval=None )
• setResult( )
• hyparname( k )