Bayesian Computation

Bayesian computation aims to efficiently estimate probability distributions, particularly posterior distributions in Bayesian inference, often tackling high-dimensional problems where traditional methods struggle. Current research emphasizes developing and improving algorithms like Markov Chain Monte Carlo (MCMC), variational inference, and novel sampling techniques using diffusion models and probabilistic computing hardware (e.g., p-bits and neuromorphic chips). These advancements are driven by the need for scalable and efficient solutions in diverse fields, including machine learning, scientific modeling, and signal processing, ultimately improving the accuracy and reliability of probabilistic inference.