TransportMaps.Distributions.Examples.LogisticRegression._LogisticRegressionDistributions

Module Contents

Classes

BayesianLogisticRegression

Given a log-likelihood and a prior, assemble the posterior density

class TransportMaps.Distributions.Examples.LogisticRegression._LogisticRegressionDistributions.BayesianLogisticRegression(logL, prior)

Bases: TransportMaps.Distributions.Inference.InferenceBase.BayesPosteriorDistribution

Given a log-likelihood and a prior, assemble the posterior density

Given the log-likelihood \(\log\pi({\bf y}\vert{\bf x})\) and the prior density \(\pi({\bf x})\), assemble the Bayes’ posterior density

\[\pi({\bf x}\vert {\bf y}) \propto \pi({\bf y}\vert{\bf x}) \pi({\bf x})\]

Parameters:

• logL (LogLikelihood) – log-likelihood \(\log\pi({\bf y}\vert{\bf x})\)

• prior (Distribution) – prior density \(\pi({\bf x})\)