elbo_monitoring_VEM: Evidence Lower Bound maximised in MagmaClust

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

Evidence Lower Bound maximised in MagmaClust

Description

Evidence Lower Bound maximised in MagmaClust

Usage

elbo_monitoring_VEM(hp_k, hp_i, db, kern_i, kern_k, hyperpost, m_k, pen_diag)

Arguments

hp_k	A tibble, data frame or named vector of hyper-parameters for each clusters.
hp_i	A tibble, data frame or named vector of hyper-parameters for each individuals.
db	A tibble containing values we want to compute elbo on. Required columns: Input, Output. Additional covariate columns are allowed.
kern_i	Kernel used to compute the covariance matrix of individuals GPs at corresponding inputs.
kern_k	Kernel used to compute the covariance matrix of the mean GPs at corresponding inputs.
hyperpost	A list of parameters for the variational distributions of the K mean GPs.
m_k	Prior value of the mean parameter of the mean GPs (mu_k). Length = 1 or nrow(db).
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

Value of the elbo that is maximised during the VEM algorithm used for training in MagmaClust.

Examples

TRUE

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