logL_monitoring: Log-Likelihood for monitoring the EM algorithm in Magma

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

Log-Likelihood for monitoring the EM algorithm in Magma

Description

Log-Likelihood for monitoring the EM algorithm in Magma

Usage

logL_monitoring(
  hp_0,
  hp_i,
  db,
  m_0,
  kern_0,
  kern_i,
  post_mean,
  post_cov,
  pen_diag
)

Arguments

hp_0	A named vector, tibble or data frame, containing the hyper-parameters associated with the mean GP.
hp_i	A tibble or data frame, containing the hyper-parameters with the individual GPs.
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.
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.
pen_diag	A jitter term that is added to the covariance matrix to avoid numerical issues when inverting, in cases of nearly singular matrices.

Value

A number, expectation of joint log-likelihood of the model. This quantity is supposed to increase at each step of the EM algorithm, and thus used for monitoring the procedure.

Examples

TRUE

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