mutRate

Computes the mutation-rate vector Q_P according to three scalable
schemes proposed in the reference paper.

Implements scalable Markov chain Monte Carlo (Sca-MCMC) algorithms for Bayesian inference in dynamic generalized linear models (DGLMs). The package supports Pareto-type and Gamma-type DGLMs, which are suitable for modeling heavy-tailed phenomena such as wealth allocation and financial returns. It provides simulation tools for synthetic DGLM data, adaptive mutation-rate strategies (ScaI, ScaII, ScaIII), geometric temperature ladders for parallel tempering, and posterior predictive evaluation metrics (e.g., R2, RMSE). The methodology is based on the scalable MCMC framework described in Guo et al. (2025).

Guangbao Guo

SDGLM

Scalable Bayesian Inference for Dynamic Generalized Linear
Models

X. Meggie Wen

Lixing Zhu

mutRate function

<dl><dt>type</dt>
<dd>Character, one of "ScaI", "ScaII", "ScaIII".</dd>
<dt>N</dt>
<dd>Integer &gt; 1, number of parallel chains.</dd>
<dt>L</dt>
<dd>Integer &gt; 0, length of the parameter vector.</dd>
<dt>beta.star</dt>
<dd>Target parameter vector (binary or factor).</dd>
<dt>beta0</dt>
<dd>Initial parameter vector (same length as beta.star).</dd></dl>

Arguments

Scalable Mutation-Rate Strategies for Sca-MCMC — mutRate

<dl>

<dt>type</dt>
<dd>Character, one of "ScaI", "ScaII", "ScaIII".</dd>


<dt>N</dt>
<dd>Integer &gt; 1, number of parallel chains.</dd>


<dt>L</dt>
<dd>Integer &gt; 0, length of the parameter vector.</dd>


<dt>beta.star</dt>
<dd>Target parameter vector (binary or factor).</dd>


<dt>beta0</dt>
<dd>Initial parameter vector (same length as beta.star).</dd>

</dl>

mutRate: Scalable Mutation-Rate Strategies for Sca-MCMC

Description

Usage

Value

Arguments

Examples