## Bayesian Inference, Volume 1Nicholas G. Polson, George C. Tiao This is a two-volume set of a collection of 30 papers presenting ideas which have now become standard in the field of Bayesian Inference. Topics covered include the central field of statistical inference, applications in areas of probability theory, information theory, and computational theory. |

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alternative application approximation assumed assumption asymptotic Bayes Bayesian analysis Bayesian inference Bayesian statistics Bernardo Box's BRUNO DE FINETTI choice classical conditional consider contingency tables convex corresponding decision defined definition degree of belief density Discussion of Professor Equation evaluation example expected experiment experimenter FINETTI finite follows forecasts frequency frequentist given independent interactions interest L. J. Savage likelihood function likelihood principle likelihood ratio linear logic Math mathematical maximum entropy mean measure method normal distribution null hypothesis observations obtained opinion outliers paper parameter personal probabilities possible posterior probability posterior robustness predictive distribution prequential prior distribution prior probability probability distribution problem procedure Professor Box proper scoring rules random quantities reasonable reference posterior distribution reference prior Robust Bayesian Viewpoint sample Section sequential simple situation specific Statistical Inference statistician Suppose theorem theory Tiao tion true TRUTH AND PROBABILITY uncertainty variance vector