* The on-line textbook: Information Theory, Inference, and Learning Algorithms, by David MacKay, contains chapters on elementary error-correcting codes ; on the theoretical limits of error-correction ; and on the latest state-of-the-art error-correcting codes, including low-density parity-check codes, turbo codes, and fountain codes.

* The on-line textbook: Information Theory, Inference, and Learning Algorithms, by David J. C. MacKay, discusses Bayesian model comparison in Chapters 3 and 28.

* The on-line textbook: Information Theory, Inference, and Learning Algorithms, by David J. C. MacKay includes simple examples of the EM algorithm such as clustering using the soft k-means algorithm, and emphasizes the variational view of the EM algorithm, as described in Chapter 33. 7 of version 7. 2 ( fourth edition ).

* The on-line textbook: Information Theory, Inference, and Learning Algorithms, by David J. C. MacKay, discusses LDPC codes in Chapter 47.

* The on-line textbook: Information Theory, Inference, and Learning Algorithms, by David J. C. MacKay, discusses sparse-graph codes in Chapters 47-50.

* The on-line textbook: Information Theory, Inference, and Learning Algorithms, by David J. C. MacKay.

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