A variety of problems in machine learning and digital communication deal with complex but structured natural or artificial systems. In this book, Brendan Frey uses graphical models as an overarching framework to describe and solve problems of pattern classification, unsupervised learning, data compression, and channel coding. Using probabilistic structures such as Bayesian belief networks and Markov random fields, he is able to describe the relationships between random variables in these systems and to apply graph-based inference techniques to develop new algorithms.

机器学习和数字通信中的各种问题涉及复杂但结构化的自然或人工系统。在本书中，Brendan Frey使用图形模型作为总体框架来描述和解决模式分类、无监督学习、数据压缩和信道编码等问题。利用概率结构，如贝叶斯信念网络和马尔可夫随机场，他能够描述这些系统中随机变量之间的关系，并应用基于图形的推理技术开发新算法。