I've been reading through this bit by bit the last few days, mostly to get a handle on how to implement MCMC for Bayesian posteriors, and I have to say its fantastically written. I wouldn't call it comprehensive or unbiased, but it sets up the infrastructure of interrelatedness between noisy channels, information theory, statistics, and machine learning pretty much as effortlessly as possible.
Note: I'm buying it entirely because it has wide margins. Many of the calculations he outlines deserve to be worked out in full. Wide margins are absolutely the most important publishing concern for a math/science/engineering-based text.
If you want to learn how to implement MCMC I recommend:
Bayesian Logical Analysis Physical Sciences by Gregory
Gregory's book explains a lot more of the engineering (autocorrelations, step size jumping, etc..). Even better, it discusses how to perform model selection using a clever annealing technique. Though model selection may not be of interest to you.
ps - MacKay's book is my nightly reading, so I'm not dissing MacKay :)
Note: I'm buying it entirely because it has wide margins. Many of the calculations he outlines deserve to be worked out in full. Wide margins are absolutely the most important publishing concern for a math/science/engineering-based text.