`altar.models.Null`

Module Contents

Classes

class altar.models.Null.Null(name, locator, **kwds)

Bases: altar.models.Bayesian.Bayesian

A trivial model that can be used as a base class for deriving interesting ones

parameters

doc = the number of model degrees of freedom

initializeSample(self, step): Fill {step.θ} with an initial random sample from my prior distribution

priorLikelihood(self, step): Fill {step.prior} with the likelihoods of the samples in {step.theta} in the prior distribution

dataLikelihood(self, step): Fill {step.data} with the likelihoods of the samples in {step.theta} given the available data. This is what is usually referred to as the “forward model”

verify(self, step, mask): Check whether the samples in {step.theta} are consistent with the model requirements and update the {mask}, a vector with zeroes for valid samples and non-zero for invalid ones

forwardProblem(self, application, theta=None): Perform the forward modeling with given {theta}