Designed for researchers and graduate students in machine learning, this book introduces the theory of variational Bayesian learning, a popular machine learning method, and suggests how to make use of it in practice. Detailed derivations allow readers to follow along without prior knowledge of the specific mathematical techniques.

《高等院校统计学专业规划教材•贝叶斯统计》共六章，可分二部分。前三章围绕先验分布介绍贝叶斯推断方法。后三章围绕损失函数介绍贝叶斯决策方法。阅读这些内容仅需要概率统计基本知识就够了。《高等院校统计学专业规划教材•贝叶斯统计》力图用生动有趣的例子来说明贝叶斯统计的基本思想和基本方法，尽量使读者对贝叶斯统计产生兴趣，引发读者使用贝叶方法去认识和解决实际问题的愿望。进而去丰富和发展贝叶斯统计。假如学生的兴趣被钓出来，愿望被引出来，那么讲授这一门课的目的也基本达到了。

The standard rules of probability can be interpreted as uniquely valid principles in logic. In this book, E. T. Jaynes dispels the imaginary distinction between 'probability theory' and 'statistical inference', leaving a logical unity and simplicity, which provides greater technical power and flexibility in applications. This book goes beyond the conventional mathematics of probability theory, viewing the subject in a wider context. New results are discussed, along with applications of probability theory to a wide variety of problems in physics, mathematics, economics, chemistry and biology. It contains many exercises and problems, and is suitable for use as a textbook on graduate level courses involving data analysis. The material is aimed at readers who are already familiar with applied mathematics at an advanced undergraduate level or higher. The book will be of interest to scientists working in any area where inference from incomplete information is necessary.