## An introduction to probability theory and its applications, Volume 1Major changes in this edition include the substitution of probabilistic arguments for combinatorial artifices, and the addition of new sections on branching processes, Markov chains, and the De Moivre-Laplace theorem. |

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#### LibraryThing Review

User Review - redgiant - LibraryThingIf you were to lock me up for a year and allow only one book for the whole time, this is the book I would take with me. The way each problem is treated is delightful. The book is slightly dated and so ... Read full review

#### LibraryThing Review

User Review - bluetyson - LibraryThingA really, reall dull mathematics text. An important book, but this one you will not be pleased with having to read, or at least I never came across anyone that was, when I had to use it. Highly detailed and quite complex look at the probability subject for the tertiary level beginner. Read full review

### Contents

chapter Page | 1 |

The Sample Space | 7 |

Elements of Combinatorial Analysis | 26 |

Copyright | |

105 other sections not shown

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### Common terms and phrases

applies arbitrary assume balls Bernoulli trials binomial coefficient binomial distribution calculations cards cells central limit theorem chance fluctuations chapter coefficients coin compound Poisson distribution conditional probability consider contains corresponding defined denote derived differential equations elements epoch exactly example expected number Find the probability finite fixed follows formula function genes genotypes geometric distribution given hence inequality infinite integer intuitive large numbers law of large lemma limit theorem Markov chains matrix means mutually independent nth trial number of successes occurs particle path of length player Poisson distribution population positive possible preceding probability distribution probability theory problem proof prove random walk recurrent event replaced represents result right side sample points sample space satisfy solution statistics Stirling's formula stochastic stochastic processes stochastically independent Suppose tossing transition probabilities trial number values