ULUstateSample.mpfr

ZMUt

single observation state random perturbation

SOS.random

Sample hidden states from hidden Markov multilinear model with precision using Rmpfr package

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.

Jong Hee Park

NetworkChange

Bayesian Package for Network Changepoint Analysis

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