The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
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最大似然估计(和其他类似方法)把待估计的参数看作是确定性的量,只是其取值未知。最佳估计就是使得产生已观测到的样本(即训练样本)的概率为最大的那个值。 与此不同的是,贝叶斯估计则把待估计的参数看成是符合某种先验分布的随机变量。对样本进行观测的过程,就是把先验概率密度转化为后验概率密度,这样就利用样本的信息修正了对参数的初始估计值。
参数估计问题是统计学中的经典问题,并且已经有了一些具体的解决方法。这里我们将主要讨论两种最常用和很有效的方法,也就是:最大似然估计和贝叶斯估计。
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