Overview of Research on Bayesian Inference and Parallel Tempering

Bayesian inference is one of the main problems in statistics.It aims to update the prior knowledge of the probability distribution model based on the observation data.For the posterior probability that cannot be observed or is difficult to directly calculate, which is often encountered in real situations, Bayesian inference can obtain a good approximation.It is a kind of important method based on Bayesian theorem.Many machine learning problems involve the process of simulating and approximating the target distribution of various types of feature data, such as classification models, topic modeling, and data mining.Therefore, Bayesian inference has shown important and unique research value in the field of machine learning.With the beginning of the big data era, the experimental data collected by researchers through actual information is very large, resulting in the complex distribution of targets to be simulated and calculated.How to perform accurate and time-efficient approximation inferences on target distrib