Adaptive Metropolis

Adaptive Metropolis algorithms aim to improve the efficiency of Markov Chain Monte Carlo (MCMC) methods by dynamically adjusting proposal distributions based on the sampled history. Current research focuses on developing adaptive strategies using reinforcement learning, optimizing proposal distributions through techniques like preconditioning with Fisher information, and incorporating cyclical stepsize schemes to enhance exploration and prevent convergence to local modes. These advancements lead to more robust and efficient sampling, particularly beneficial for high-dimensional problems and complex posterior distributions encountered in Bayesian inference and machine learning applications.