mvn_inference: Performs inference over a multivariate normal distribution

In dbnR: Dynamic Bayesian Network Learning and Inference

Performs inference over a multivariate normal distribution

Description

Given some evidence, this function performs inference over a multivariate normal distribution. After converting a Gaussian linear network to its MVN form, this kind of inference can be performed. It's recommended to use predict_dt functions instead unless you need a more flexible inference method.

Usage

mvn_inference(mu, sigma, evidence)

Arguments

mu	the mean vector
sigma	the covariance matrix
evidence	a single row data.table or a named vector with the values and names of the variables given as evidence

Value

a list with the posterior mean and covariance matrix

Examples

size = 3
data(motor)
dt_train <- motor[200:2500]
dt_val <- motor[2501:3000]
obj <- c("pm_t_0")

net <- learn_dbn_struc(dt_train, size)
f_dt_train <- fold_dt(dt_train, size)
f_dt_val <- fold_dt(dt_val, size)
ev <- f_dt_val[1, .SD, .SDcols = obj]
fit <- fit_dbn_params(net, f_dt_train, method = "mle-g")

pred <- mvn_inference(calc_mu(fit), calc_sigma(fit), ev)

dbnR documentation built on Oct. 5, 2022, 1:07 a.m.