贝叶斯推理与并行回火研究综述

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 distributions under complex data has become a major and difficult point in Bayesian inference problems today. Aiming at the inference problem under this complex distribution model, this paper systematically introduces and summarizes the two main methods for solving Bayesian inference problems in recent years, which are variational inference and sampling methods. Firsly, this paper gives the problem definition and theoretical knowledge of variational inference, introduces in detail the variational inference algorithm based on coordinate ascent, and gives the existing applications and future prospects of this method. Next, it reviews the research results of existing sampling methods at home and abroad, gives the specific algorithm procedure of various main sampling methods, as well as summarizes and compares the characteristics, advantages and disadvantages of these methods. Finally, this paper introduces parallel tempering technique, outlines its basic theories and methods, discusses the combination and application of parallel tempering and sampling methods, and explores new research directions for the future development of Bayesian inference problems.