An introduction to information theory: symbols, signals and noise. Despite being fairly advanced, the writing style is tutorial in nature. A straightforward (and clearly laid out) account of inference, which compares Bayesian and non-Bayesian approaches. Statistical Inference: An Integrated Approach. A very readable text that roams far and wide over many topics, almost all of which make use of Bayes’ rule. The modern classic on information theory. Information theory, inference, and learning algorithms. A rigorous and comprehensive text with a strident Bayesian style. Salman Khan’s online mathematics videos make a good introduction to various topics, including Bayes’ rule. Khan, S, 2012, Introduction to Bayes’ Theorem. Its discursive style makes it long (600 pages) but never dull,and it is packed ful l of insights.
Probability Theory: The Logic of Science. A rigorous and comprehensive account of Bayesian analysis, with many real-world examples.
Gelman A, Carlin J, Stern H, and Rubin D. Provides tutorial material on Bayes’ rule and a lucid analysis of theĭistinction between Bayesian and frequentist statistics. Understanding Psychology as a Science: An Introduction to Scientific and Statistical Inference. An excellent non-Bayesian introduction to statistical analysis.ĭienes, Z (2008) 8.
As the title suggests, this is mainly about machine learning, but it provides a lucid and comprehensive account of Bayesian methods.Ĭowan G (1998) 6. Pattern Recognition and Machine Learning. Bayesian Theory A rigorous account of Bayesian methods, with many real-world examples.īishop, C (2006) 5. Of all the books listed below it strives hardest to give an intuitive grasp of the essential ideas, but it still requires some mathematical sophistication from page 1.īelow is a list of Further Readings from my book, with comments on each publication.īernardo, JM and Smith, A, (2000) 4. If I had to choose a single text for a beginner, it would be Sivia DS and Skilling J (2006) book (see below).
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