What books provide an overview of computational statistics as it applies to computer science?

As a software engineer, I’m interested in topics such as statistical algorithms, data mining, machine learning, Bayesian networks, classification algorithms, neural networks, Markov chains, Monte Carlo methods, and random number generation.

I personally haven’t had the pleasure of working hands-on with any of these techniques, but I have had to work with software that, under the hood, employed them and would like to know more about them, at a high level. I’m looking for books that cover a great breadth – great depth is not necessary at this point. I think that I can learn a lot about software development if I can understand the mathematical foundations behind the algorithms and techniques that are employed.

Can the Statistical Analysis community recommend books that I can use to learn more about implementing various statistical elements in software?

Answer

I’d suggest Christopher Bishop’s “Pattern Recognition and Machine Learning”. You can see some of it, including a sample chapter, at https://www.microsoft.com/en-us/research/people/cmbishop/#!prml-book

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