Principles of UncertaintyCRC Press, 18 de mai. de 2011 - 504 páginas An intuitive and mathematical introduction to subjective probability and Bayesian statistics.An accessible, comprehensive guide to the theory of Bayesian statistics, Principles of Uncertainty presents the subjective Bayesian approach, which has played a pivotal role in game theory, economics, and the recent boom in Markov Chain Monte Carlo methods. |
Conteúdo
Probability | 1 |
Conditional Probability and Bayes Theorem | 29 |
Discrete Random Variables | 79 |
Continuous Random Variables | 117 |
Transformations | 185 |
Normal Distribution | 233 |
Making Decisions | 267 |
Conjugate Analysis | 299 |
Hierarchical Structuring of a Model | 335 |
Markov Chain Monte Carlo | 351 |
Multiparty Problems | 379 |
Exploration of Old Ideas | 435 |
Applications | 447 |
Bibliography | 449 |
Back Cover
| 465 |
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Termos e frases comuns
6-fine partition analysis applies assumption Bayes Theorem Bayesian binomial binomial distribution Chapter characteristic function choice choose coin complex numbers compute conditional consequence consider convergence countable additivity cumulative distribution function decision defined density Dick discussion equation example expected utility finite number flip follows given Hence independent induction infinite interval Jane Jane’s Kadane large numbers law of large Lemma likelihood limit linear Markov Chain mathematical matrix maximize McShane integrable mean Metropolis-Hastings algorithm natural numbers non-negative normal distribution optimal orthogonal orthogonal matrix outcome P{AB Paradox player posterior distribution precision matrix prior probability problem Proof prove rational numbers real numbers respect result Riemann integral Riemann-Stieltjes integral sample satisfies sequence Similarly Statistical strategy Summary Suppose sure loser sure loss Theorem theory tickets utility function variance vector zero