ULUstateSample.mpfr

Sample hidden states from hidden Markov multilinear model with precision using Rmpfr package.
Uses vectorized operations for improved performance.

Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn (2020)). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided. This version includes performance optimizations with vectorized MCMC operations and modern ggplot2-based visualizations with colorblind-friendly palettes.

Jong Hee Park

NetworkChange

Bayesian Package for Network Changepoint Analysis

Yunkyu Sohn

ULUstateSample.mpfr function

<dl><dt>m</dt>
<dd>The number of break</dd>
<dt>s</dt>
<dd>Latent state vector</dd>
<dt>ZMUt</dt>
<dd>Z - MU</dd>
<dt>s2</dt>
<dd>error variance</dd>
<dt>P</dt>
<dd>Transition matrix</dd>
<dt>SOS.random</dt>
<dd>single observation state random perturbation</dd></dl>

Arguments

Hidden State Sampler with precision — ULUstateSample.mpfr

<dl>

<dt>m</dt>
<dd>The number of break</dd>


<dt>s</dt>
<dd>Latent state vector</dd>


<dt>ZMUt</dt>
<dd>Z - MU</dd>


<dt>s2</dt>
<dd>error variance</dd>


<dt>P</dt>
<dd>Transition matrix</dd>


<dt>SOS.random</dt>
<dd>single observation state random perturbation</dd>

</dl>

ULUstateSample.mpfr: Hidden State Sampler with precision

Description

Usage

Value

Arguments