PRML一般指代Pattern Recognition and Machine Learning一书。该书出版于2006年，是贝叶斯机器学习领域的经典之作。作者为Christopher M. Bishop，现为剑桥微软研究院实验室主任。 大牛推荐语：这是第一本从贝叶斯角度讲述模式识别以及机器学习的基础教材，它被广泛用作国外大学的标准教材。作为一本基础教材，它假设读者没有机器学习的背景，并且详细阐述了机器学习领域经常用到的模型。 图书基本信息 Pattern Recognition and Machine Learning 简装 备注： 简装版，精装版ISBN为9780387310732 作者： Christopher M. Bishop; ISBN13： 9781493938438 类型： 平装(简装书) 语种： 英语（English） 出版日期： 2016-08-23 出版社： Springer 页数： 738 重量（克）： 1619 尺寸： 24.1554 x 17.526 x 4.1148 cm 商品简介 Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same ?eld, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation pro- gation. Similarly, new models based on kernels have had signi?cant impact on both algorithms and applications. This new textbook re?ects these recent developments while providing a comp- hensive introduction to the ?elds of pattern recognition and machine learning. It is aimed at advanced undergraduates or ?rst year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or - chine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not - sential as the book includes a self-contained introduction to basic probability theory.