gr_sum_logL_GP_clust: Gradient of the mixture of Gaussian likelihoods

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

Gradient of the mixture of Gaussian likelihoods

Description

Compute the gradient of a sum of Gaussian log-likelihoods, weighted by their mixture probabilities.

Usage

gr_sum_logL_GP_clust(hp, db, mixture, mean, kern, post_cov, pen_diag)

Arguments

hp	A tibble, data frame or named vector of hyper-parameters.
db	A tibble containing data we want to evaluate the logL on. Required columns: Input, Output. Additional covariate columns are allowed.
mixture	A tibble or data frame, indicating the mixture probabilities of each cluster for the new individual/task.
mean	A list of hyper-posterior mean parameters for all clusters.
kern	A kernel function.
post_cov	A list of hyper-posterior covariance parameters for all clusters.
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 named vector, corresponding to the value of the hyper-parameters' gradients for the mixture of Gaussian log-likelihoods involved in the prediction step of MagmaClust.

Examples

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