An Introduction to Probability Theory and Its Applications, Volume 1A complete guide to the theory and practical applications of probability theory An Introduction to Probability Theory and Its Applications uniquely blends a comprehensive overview of probability theory with the real-world application of that theory. Beginning with the background and very nature of probability theory, the book then proceeds through sample spaces, combinatorial analysis, fluctuations in coin tossing and random walks, the combination of events, types of distributions, Markov chains, stochastic processes, and more. The book's comprehensive approach provides a complete view of theory along with enlightening examples along the way. |
Contents
CHAPTER PAGE | 1 |
THE SAMPLE SPACE | 7 |
ELEMENTS OF COMBINATORIAL ANALYSIS | 26 |
Copyright | |
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a₁ applies arbitrary assume balls Bernoulli trials binomial coefficient binomial distribution Bose-Einstein statistics cards cells chapter coefficients coin conditional probability consider corresponding defined denote derived dice digits elements epoch equally probable equations example experiment Find the probability finite follows frequencies function genes genotypes geometric distribution given hence inequality infinite integer intuitive k₁ large numbers law of large lemma limit theorem Markov chains means mutually independent n₁ normal approximation nth trial number of successes occurs outcomes P₁ pairs pairwise independent particle player Poisson distribution population possible probability distribution probability theory problem proof prove r₁ r₂ random variables random walk recurrent event replacement represents result S₁ sample points sample space sequence solution statistics Stirling's formula stochastic stochastically independent Suppose theory tossing total number values X₁