geoTemp

Produces a geometric progression of inverse-temperatures (or temperatures)
commonly used in parallel-tempered MCMC algorithms.

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

geoTemp function

<dl><dt>N</dt>
<dd>Integer &gt; 1, number of chains/temperatures.</dd>
<dt>T1</dt>
<dd>Numeric &gt; 0, coldest temperature (usually 1).</dd>
<dt>TN</dt>
<dd>Numeric &gt; T1, hottest temperature.</dd></dl>

Arguments

Generate Geometric Temperature Ladder for Parallel Tempering — geoTemp

<dl>

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


<dt>T1</dt>
<dd>Numeric &gt; 0, coldest temperature (usually 1).</dd>


<dt>TN</dt>
<dd>Numeric &gt; T1, hottest temperature.</dd>

</dl>

geoTemp: Generate Geometric Temperature Ladder for Parallel Tempering

Description

Usage

Value

Arguments

Examples