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

In ArthurLeroy/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

ArthurLeroy/MagmaClustR documentation built on Dec. 24, 2024, 11:18 a.m.