ExpectationPropagation: Use Expectation Propogation to Approximate mean & covariance

In lcrawlab/SINATRA: Sub Image Analysis using Topological Summary Statistics (SINATRA)

Use Expectation Propogation to Approximate mean & covariance

Description

ExpectationPropogation Approximates the latent posterior with a Gaussian distributions; it does so by moment matching. Pseudocode taken from Rasmussen and Williams, Chapter 3. This function outputs the mean and covariance of the approximated posterior. To actually generate samples from the latent posterior, generate samples from a multivariate normal with the parameters returned by this function.

Usage

ExpectationPropagation(K, class_labels)

Arguments

K	(matrix): the covariance matrix for the GP model
class_labels	(vector): +/- 1 values indicating the class labels of the data points

Value

params (list): list of the posterior mean and variances.

lcrawlab/SINATRA documentation built on Sept. 13, 2023, 2 p.m.