TransportMaps.Distributions.Examples.BurgersPDE.BurgersDistributions

Module Contents

Classes

ViscosityInitialConditionsBurgersPosteriorDistribution

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

class TransportMaps.Distributions.Examples.BurgersPDE.BurgersDistributions.ViscosityInitialConditionsBurgersPosteriorDistribution(solver, u0_length_scale=1.0, nu_mean=-3, nu_std=3, obs_sigma=0.01, obs=None)

Bases: TransportMaps.Distributions.Inference.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 \(\pi({\bf x})\)