e_step: E-Step of the EM algorithm

In MagmaClustR: Clustering and Prediction using Multi-Task Gaussian Processes with Common Mean

E-Step of the EM algorithm

Description

Expectation step of the EM algorithm to compute the parameters of the hyper-posterior Gaussian distribution of the mean process in Magma.

Usage

e_step(db, m_0, kern_0, kern_i, hp_0, hp_i, pen_diag)

Arguments

db	A tibble or data frame. Columns required: ID, Input, Output. Additional columns for covariates can be specified.
m_0	A vector, corresponding to the prior mean of the mean GP.
kern_0	A kernel function, associated with the mean GP.
kern_i	A kernel function, associated with the individual GPs.
hp_0	A named vector, tibble or data frame of hyper-parameters associated with kern_0.
hp_i	A tibble or data frame of hyper-parameters associated with kern_i.
pen_diag	A number. A jitter term, added on the diagonal to prevent numerical issues when inverting nearly singular matrices.

Value

A named list, containing the elements mean, a tibble containing the Input and associated Output of the hyper-posterior's mean parameter, and cov, the hyper-posterior's covariance matrix.

Examples

TRUE

MagmaClustR documentation built on June 29, 2024, 1:06 a.m.