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Selected books in informatics

Many volumes appear on practical methods in computation, data mining, and related topics. Sample entries follow below. The ASAIP Editors are seeking a volunteer among ASAIP members to draft this list.

Contact the ASAIP editors here

The BUGS Book: A Practical Introduction to Bayesian Analysis

    by David Lunn, Chris Jackson, Nicky Best, Andrew Thomas, and David Spiegelhalter (Chapman & Hall/CRC, 2012).  By the developers of BUGS software (Bayesian Inference Using Gibbs Sampling), this volumes introduces Bayesian analysis including prediciton, missing data, model criticism and prior sensitivity.  Includes many worked examples and exercises.

Machine Learning: An Algorithmic Perspective

    by Stephen Marsland (CRC Press, 2009).  A readable, intermediate-level book with examples in Python.  Topics include the multi-layer perceptron, radial basis functions and splines, Support Vector Machines,  CART and decision trees, boosting and bagging, unsupervised learning, dimensionality reduction, optimization methods, evolutionary and reinforcement learning, Markov Chain Monte Carlo, hidden Markov and other graphical models.