Research Progress in Bayesian Program Learning

Abstract

Bayesian Program Learning (BPL) is an important area of machine learning. In recent years, the small sample learning model with BPL as the core has made breakthroughs in methodology and performance, attracting great attention from both industry and academia. This paper reviews the research progress and application of BPL. First of all, it introduces the research background, development history and research route of BPL. Secondly, a brief overview of Bayesian model, reasoning algorithm, based on this, a detailed review of Bayesian learning based on speech, assembly, motion learning, bias diagnosis, learning effectiveness assessment, classification and so on, to explain the application of the method, tools, experimental results, and a brief overview of open source tools for probability programming. Finally, the paper summarizes and points out the future research direction of BPL.