`altar.models.Bayesian`

Module Contents

Classes

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

Bases: altar.component

The base class of AlTar models that are compatible with Bayesian explorations

offset

doc = the starting point of my state in the overall controller state

parameters

doc = the number of model degrees of freedom

psets

default

doc = an ensemble of parameter sets in the model

rng

controller

job

info

warning

error

default

firewall

initialize(self, application): Initialize the state of the model given an {application} context

posterior(self, application): Sample my posterior distribution

initializeSample(self, step): Fill {step.theta} 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”

posteriorLikelihood(self, step): Given the {step.prior} and {step.data} likelihoods, compute a generalized posterior using {step.beta} and deposit the result in {step.post}

likelihoods(self, annealer, step): Convenience function that computes all three likelihoods at once given the current {step} of the problem

abstract 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

top(self, annealer): Notification that a β step is about to start

bottom(self, annealer): Notification that a β step just ended

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

mountInputDataspace(self, pfs): Mount the directory with my input files

restrict(self, theta): Return my portion of the sample matrix {theta}