m_step: M-Step of the EM algorithm

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

M-Step of the EM algorithm

Description

Maximisation step of the EM algorithm to compute hyper-parameters of all the kernels involved in Magma.

Usage

m_step(
  db,
  m_0,
  kern_0,
  kern_i,
  old_hp_0,
  old_hp_i,
  post_mean,
  post_cov,
  common_hp,
  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.
old_hp_0	A named vector, tibble or data frame, containing the hyper-parameters from the previous M-step (or initialisation) associated with the mean GP.
old_hp_i	A tibble or data frame, containing the hyper-parameters from the previous M-step (or initialisation) associated with the individual GPs.
post_mean	A tibble, coming out of the E step, containing the Input and associated Output of the hyper-posterior mean parameter.
post_cov	A matrix, coming out of the E step, being the hyper-posterior covariance parameter.
common_hp	A logical value, indicating whether the set of hyper-parameters is assumed to be common to all indiviuals.
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 hp_0, a tibble containing the hyper-parameters associated with the mean GP, hp_i, a tibble containing the hyper-parameters associated with the individual GPs.

Examples

TRUE

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