Bayesian spatial statistics and modeling represent a robust inferential framework where uncertainty in spatial processes is explicitly quantified through probability distributions. This approach ...

Empowered by technological progress, sports teams and bookmakers strive to understand relationships between player and team activity and match outcomes. For this purpose, the probability of an event ...

Background Conventionally, frequentist approach has been used to model health state valuation data. Recently, researchers started to explore the use of Bayesian methods in this area. Objectives This ...

The R package bgms provides tools for Bayesian analysis of the ordinal Markov random field, a graphical model describing a network of binary and/or ordinal variables (Marsman et al., 2025). A ...

⚠️ Pre-publication Release: The methods and results are under review. Use with appropriate caution for research applications. This repository is the companion of our pre-print "Hierarchical Bayesian ...

This study examined the relationship between the Monetary Policy Rate (MPR) and inflation across five continents from 2014 to 2023 using both Frequentist and Bayesian Linear Mixed Models (LMM). It ...