generate_temperature

Generate Geometric Inverse-Temperature Ladder
Constructs a geometric sequence of temperatures (inverse temperatures)
for parallel-tempering MCMC.

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

generate_temperature function

<dl><dt>N_chain</dt>
<dd>Number of parallel chains (N_chain &gt; 1).</dd>
<dt>T_max</dt>
<dd>Highest temperature (coldest chain). Default is 10.</dd></dl>

Arguments

Generate Geometric Inverse-Temperature Ladder
Constructs a geometric sequence of temperatures (inverse temperatures)
for parallel-tempering MCMC. — generate_temperature

<dl>

<dt>N_chain</dt>
<dd>Number of parallel chains (N_chain &gt; 1).</dd>


<dt>T_max</dt>
<dd>Highest temperature (coldest chain). Default is 10.</dd>

</dl>

generate_temperature: Generate Geometric Inverse-Temperature Ladder Constructs a geometric sequence of temperatures (inverse temperatures) for parallel-tempering MCMC.

Description

Usage

Value

Arguments

Examples