TransportMaps.Distributions.Examples.RailwayVehicleDynamics.Coradia175.Coradia175VehicleParametersEstimation

Module Contents

Classes

ParametersLogLikelihood	Abstract class for log-likelihood \(\log \pi({\bf y} \vert {\bf x})\)
ParametersPosterior	Given a log-likelihood and a prior, assemble the posterior density

class TransportMaps.Distributions.Examples.RailwayVehicleDynamics.Coradia175.Coradia175VehicleParametersEstimation.ParametersLogLikelihood(y, vehicle, par_name_list)

Bases: TransportMaps.Likelihoods.LikelihoodBase.LogLikelihood

Abstract class for log-likelihood \(\log \pi({\bf y} \vert {\bf x})\)

Note that \(\log\pi:\mathbb{R}^d \rightarrow \mathbb{R}\) is considered a function of \({\bf x}\), while the data \({\bf y}\) is fixed.

Parameters:

• y (ndarray) – data

• dim (int) – input dimension $d$

property vehicle

property par_name_list

evaluate(x, *args, **kwargs)

[Abstract] Evaluate \(\log\pi({\bf y} \vert {\bf x})\).

Parameters:

x (ndarray [\(m,d\)]) – evaluation points

Returns:

(ndarray [\(m,1\)]) – function evaluations

grad_x(x, *args, **kwargs)

[Abstract] Evaluate \(\nabla_{\bf x}\log\pi({\bf y} \vert {\bf x})\).

Parameters:

x (ndarray [\(m,d\)]) – evaluation points

Returns:

(ndarray [\(m,1,d\)]) – gradient evaluations

tuple_grad_x(x, *args, **kwargs)

Evaluate \(\left(\log\pi({\bf y} \vert {\bf x}),\nabla_{\bf x}\log\pi({\bf y} \vert {\bf x})\right)\).

Parameters:

x (ndarray [\(m,d\)]) – evaluation points

Returns:

(tuple) –

\(\left(\log\pi({\bf y} \vert {\bf x}),\nabla_{\bf x}\log\pi({\bf y} \vert {\bf x})\right)\)

class TransportMaps.Distributions.Examples.RailwayVehicleDynamics.Coradia175.Coradia175VehicleParametersEstimation.ParametersPosterior(y, vehicle, par_name_list, T=None, Z=None)

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 \(\pi({\bf x})\)