Detailed Reversible Jump Markov Chain Monte Carlo (RJMCMC) Sampling Algorithm

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Resource Overview

A comprehensive implementation of the Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm, accompanied by relevant research papers for comparative analysis. The program includes multiple transition mechanisms (birth, death, split, merge, update) with detailed code comments and algorithmic explanations.

Detailed Documentation

Here we provide a detailed implementation of the Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. The program includes accompanying research papers, enabling users to cross-reference the algorithm with theoretical foundations for deeper understanding. The implementation features multiple transition mechanisms including birth moves (adding parameters), death moves (removing parameters), split moves (dividing components), merge moves (combining components), and update moves (modifying existing parameters), utilizing metropolis-hastings acceptance ratios and dimension-matching conditions to ensure detailed balance. Each transition type is implemented with corresponding proposal distributions and Jacobian adjustments for dimension changes, making it suitable for various Bayesian model selection scenarios and parameter space exploration tasks.

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