Titlebook: Inference, Method and Decision; Towards a Bayesian P Roger D. Rosenkrantz Book 1977 D. Reidel Publishing Company, Dordrecht, Holland 1977 K

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Bayes and Poppernd therefore not to be countenanced. But while Bayesians evaluate hypotheses primarily in terms of their probability, their position rests on no obscure ‘principle of induction’, but on Bayes’ rule. For a Bayesian, ‘learning from experience’ can only mean modifying prior probabilities by conditional

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Testingd sample coverage is an approximation to the average likelihood, and one that is often more convenient to use (or which can be used as a surrogate when the likelihood function cannot be computed). Moreover, the OSC has a clear and definite meaning as a measure of the improbability of a theory’s accu

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Bayes/Orthodox Comparisonsy, I treat the problem of identifying the degree of a polynomial and the order of a Markov chain. When we raise the degree of a polynomial or the order of a Markov chain, we improve the model’s accuracy at the cost of some simplicity. According to the Bayesian analysis of Chapter 5, there is a well-

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Information theory was developed by communication theorists and engineers to solve problems whose connection with efficient experimentation is less than obvious. Yet, as we will see, the connections are there all right, and it is part of our task in this chapter to articulate them.

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Bayes/Orthodox Comparisonsputable in both cases. It is worth stressing that the Bayesian approach to such problems is unified: one compares average likelihoods. By contrast, orthodox statistics offers us a mixed bag of tricks with no single (or simple) underlying logic.