bma_posterior: Compute Posterior Distributions from Graph Search Results

In BGGM: Bayesian Gaussian Graphical Models

Compute Posterior Distributions from Graph Search Results

Description

The 'bma_posterior' function samples posterior distributions of graph parameters (e.g., partial correlations or precision matrices) based on the graph structures sampled during a Bayesian graph search performed by ggm_search.

Usage

bma_posterior(object, param = "pcor", iter = 5000, progress = TRUE)

Arguments

object	A ggm_search object
param	Compute BMA on either partial correlations "pcor" (default) or on precision matrix "Theta".
iter	Number of samples to be drawn, defaults to 5,000
progress	Show progress bar, defaults to TRUE

Details

This function incorporates uncertainty in both graph structure and parameter estimation, providing Bayesian Model Averaged (BMA) parameter estimates.

Use 'bma_posterior' when detailed posterior inference on graph parameters is needed, or to refine results obtained from 'ggm_search'.

Value

A list containing posterior samples and the Bayesian Model Averaged parameter estimates.

See Also

ggm_search

BGGM documentation built on April 4, 2025, 2:30 a.m.