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Conditional of a Gaussian mixture

Source: R/gmm_ops.R
gmm_conditionalise.Rd

Computes the conditional distribution of a Gaussian mixture given fixed values of a subset of coordinates, by the Schur-complement formula applied component-wise and re-weighted by the marginal evidence \(p(\textit{x}_b)\) of each component.

Usage

gmm_conditionalise(g, given)

Arguments

g

A gmm (or gmm_fit) object.

given

A length-p numeric vector. Coordinates to condition on take their numeric value; coordinates left free are NA.

Value

A gmm object in dimension equal to the number of free coordinates.

See also

Other ops: dgmm(), gmm_canonicalise(), gmm_divergence(), gmm_kld(), gmm_marginalise(), gmm_mean(), gmm_modes(), pgmm(), rgmm()

Examples

g <- gmm(weights = c(0.5, 0.5),
         means = list(c(-1, 0), c(1, 0)),
         covariances = list(diag(2), diag(2)))
gmm_conditionalise(g, given = c(NA, 0.5))
#> <conditionalise(gmm)>: K = 2 components in p = 1 dimensions
#>   [1] w = 0.5000, |mu| = 1.0000, tr(Sigma) = 1.0000
#>   [2] w = 0.5000, |mu| = 1.0000, tr(Sigma) = 1.0000