About the Book
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 83. Chapters: Bayesian probability, Prosecutor's fallacy, Likelihood function, Bayesian inference, Naive Bayes classifier, Bayesian network, Odds ratio, Variational Bayesian methods, Ensemble Kalman filter, Principle of maximum entropy, Bayesian spam filtering, Bayes estimator, Prior probability, Conjugate prior, Checking whether a coin is fair, Bayesian game, Imprecise probability, Data assimilation, Bayesian brain, Bayes factor, Graph cuts in computer vision, Jeffreys prior, Admissible decision rule, De Finetti's theorem, Bayesian inference in phylogeny, Maximum a posteriori estimation, Approximate Bayesian computation, Bayesian experimental design, Graphical model, Bayes linear statistics, Bayesian information criterion, Bayesian linear regression, Hierarchical Bayes model, Nested sampling algorithm, Evidence under Bayes theorem, Reference class problem, Recursive Bayesian estimation, Bayesian multivariate linear regression, Posterior probability, Credible interval, Extrapolation domain analysis, Hyperprior, Leonard Jimmie Savage, Deviance information criterion, AODE, Markov logic network, Bayesian search theory, Random naive Bayes, Bayesian average, A priori, Calibrated probability assessment, Hyperparameter, Gaussian process emulator, Marginal likelihood, GLUE, Aumann's agreement theorem, Precision, Base rate, Cromwell's rule, Speed prior, Bayesian econometrics, Expectation propagation, Strong prior, Sparse binary polynomial hashing, International Society for Bayesian Analysis.
