hamming_distance

Computes the proportion of mismatches between two binary or factor vectors
of equal length.

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

hamming_distance function

<dl><dt>beta_star</dt>
<dd>Target vector (true value).</dd>
<dt>beta_init</dt>
<dd>Initial/candidate vector.</dd></dl>

Arguments

Normalized Hamming Distance — hamming_distance

<dl>

<dt>beta_star</dt>
<dd>Target vector (true value).</dd>


<dt>beta_init</dt>
<dd>Initial/candidate vector.</dd>

</dl>

hamming_distance: Normalized Hamming Distance

Description

Usage

Value

Arguments

Examples