An Introduction to Probability Theory and Its Applications, by William Feller PDF

By William Feller

ISBN-10: 0471257087

ISBN-13: 9780471257080

“If you'll in basic terms ever purchase one booklet on likelihood, this is able to be the single! ”
Dr. Robert Crossman

“This is besides whatever you need to have learn so that it will get an intuitive realizing of chance concept. ”
Steve Uhlig

“As one matures as a mathematician you can savour the amazing intensity of the cloth. ”
Peter Haggstrom

Major alterations during this variation comprise the substitution of probabilistic arguments for combinatorial artifices, and the addition of latest sections on branching tactics, Markov chains, and the De Moivre-Laplace theorem.

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Additional info for An Introduction to Probability Theory and Its Applications, Volume 1 (3rd Edition)

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2, it is tempting to consider the likelihood as a generalized density in θ, whose mode would then be the maximum likelihood estimator, and to work with this density as with a regular distribution. 5). 1) to try to circumvent the determination of a prior distribution while putting into practice the Likelihood Principle, the choice of his distribution being objective (since depending only on the distribution of the observations). 26). The derivation of objective posterior distributions actually calls for a more advanced theory of noninformative distributions (see Chapter 3), which shows that the likelihood function cannot always be considered the most natural posterior distribution.

I=1 In this case, the stopping rule is obviously incompatible with frequentist modeling since the resulting sample always leads to the rejection of the null hypothesis H0 : θ = 0 at the level 5% (see Chapter 5). On the contrary, a Bayesian approach avoids this difficulty (see Raiffa and Schlaifer (1961) and Berger and Wolpert (1988, p. 81)). 3 Derivation of the Likelihood Principle A justification of the Likelihood Principle has been provided by Birnbaum (1962) who established that it is implied by the Sufficiency Principle, conditional upon the acceptance of a second principle.

Xn−1 , θ)IIAn (x1 , . . , xn ), thus depends only on τ through the sample x1 , . . , xn . This implies the following principle. Stopping Rule Principle If a sequence of experiments, E1 , E2 , . , is directed by a stopping rule, τ , which indicates when the experiments should stop, inference about θ must depend on τ only through the resulting sample. 4 illustrates the case where two different stopping rules lead to the same sample: either the sample size is fixed to be 12, or the experiment is stopped when 9 viewers have been interviewed.

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An Introduction to Probability Theory and Its Applications, Volume 1 (3rd Edition) by William Feller

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