Parallel tempering is a generic Markov chain Monte Carlo sampling method which allows good mixing with multimodal target distributions, where conventional Metropolis-Hastings algorithms often fail.

Bio-inspired computational methods have gained popularity recently. These methods mimic the seemingly complex behavior of ...

An adaptive algorithm is a computational program which is capable of adjusting its behaviour based on the data it receives in order to complete its goals. While standard algorithms will complete tasks ...

We describe adaptive Markov chain Monte Carlo (MCMC) methods for sampling posterior distributions arising from Bayesian variable selection problems. Point-mass mixture priors are commonly used in ...

Adaptive algorithms have immensely advanced, becoming integral for innovation across multiple industries. These intelligent systems adjust content and strategies to improve the experiences of users by ...

The testing of the semiconductor dies produced by a wafer fabrication plant involves a long series of operations requiring meticulous care. The time spent performing these tests markedly affects both ...