## An introduction to probability, decision, and inference |

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### Contents

Elementary Set Theory | 3 |

Some Important Sets of Sets | 13 |

Basic Concepts | 63 |

Copyright | |

16 other sections not shown

### Common terms and phrases

approximate assertion assess assume assumptions Axiom Bayes Bayesian Bernoulli process Chapter common df conditional confidence interval confidence region conjugate prior df consider consists in rejecting continuous random variable covariance credible interval decision problem decision theory decision-maker decision-maker's defined definition denote Derive discrete random variable distribution with parameters elements Example F(xi Figure finite fractile gamma given Hence iid observations iid with common implies independent inference integral known likelihood function linear loss function lottery marginal matrix maximum likelihood estimator moment-generating function natural conjugate prior noninformative stopping Note obtain outcome partition Poisson process posterior df posterior probability procedure Proof Exercise Prove random vector reader may verify real numbers result sample satisfies Section stopping process sufficient statistic Suppose Theorem univariate Normal process variance versus