Tomoki Matsumoto

Parallel MCMC via machine learning classification and a Kullback–Leibler divergence criterion

In the era of Big Data, Markov chain Monte Carlo (MCMC) methods, which are currently essential for Bayesian estimation, face significant computational challenges owing to their sequential nature. To achieve a faster and more effective parallel computation, we emphasize the critical role of the overlapped area of the po...