Applications of Multiple-try Metropolis Algorithms
Project with Liangliang Wang
This project aims to implement novel Monte Carlo methods, including variants of multiple-try Metropolis and sequential Monte Carlo algorithms. The goal is to assess the performance of the proposed methods through a comprehensive comparison with existing algorithms using simulation studies and to apply them to real data analysis. Applicants for this project should be strong in software development with popular programming languages (e.g., R, Python, Java, JavaScript) and be familiar with Bayesian statistics and Markov chain Monte Carlo methods.