mean_conditional: Calculate conditional mean and covariance for multivariate...

In markajoc/MBCbigP: Model-Based Clustering for High-Dimensional Data

Description

Calculate conditional mean and covariance for multivariate Gaussian distributions. Consider partitioning into A and B. This function returns parameters for the distribution of B given A, which is itself another Gaussian distribution.

Usage

mean_conditional(mu_B, mu_A, x_A, sigma_AA, sigma_AB)

sigma_conditional(sigma_BB, sigma_AB, sigma_AA)

dmvnorm_conditional(x_A, x_B, mu_A, mu_B, sigma_AA, sigma_AB, sigma_BB,
  log = TRUE)

Arguments

mu_B	The mean vector for B.
mu_A	The mean vector for A.
x_A, x_B	A dataframe or matrix
sigma_AA, sigma_BB	The covariance matrix for batches A and B.
sigma_AB	The cross-covariance matrix for A and B.
log	Logical switch for log-likelihood.

Value

The relevant mean parameter for the distribution of B given A.

markajoc/MBCbigP documentation built on May 30, 2019, 8:39 a.m.