A lot of the modern research in 'big data' analysis is/was driven by physicists. Bayesian Inference is about trying to make a decision about what you can infer from an observation or series of observations, and the impetus for this came from trying to make sense of experimental results.
Two of the really great text books in the field are by physicists,
1) 'Information Theory, Inference and Learning Algorithms' by David Mackay, a physics professor at Cambridge. Perhaps the most readable and enjoyable text book I own. Certainly up there.
2) 'Pattern Recognition and Machine Learning' by Chris Bishop, now a director at Microsoft Research in Cambridge but formerly a physicist. Delightfully, under the circumstances, his PhD supervisor was Higgs (yes, the one of boson fame)!
Two of the really great text books in the field are by physicists,
1) 'Information Theory, Inference and Learning Algorithms' by David Mackay, a physics professor at Cambridge. Perhaps the most readable and enjoyable text book I own. Certainly up there.
2) 'Pattern Recognition and Machine Learning' by Chris Bishop, now a director at Microsoft Research in Cambridge but formerly a physicist. Delightfully, under the circumstances, his PhD supervisor was Higgs (yes, the one of boson fame)!