000			02540nam a2200193 a 4500
020			_a9780534366032
020			_a0534366031
082			_a519.2 _bBEA
100			_aBean,Michael A.
245			_aProbability: The Science of Uncertainty- with Applications to Investments, Insurance, and Engineering
260			_aAustralia ; _aPacific Grove, CA : _bBrooks/Cole, _c©2001.
300			_axiii, 448 pages : _billustrations ;
490			_aBrooks/Cole series in advanced mathmatics.
500			_aIncludes index.
505			_aWhat Is Probability? -- How Is Uncertainty Quantified? -- Probability in Engineering and the Sciences -- What Is Actuarial Science? -- What Is Financial Engineering? -- Interpretations of Probability -- Probability Modeling in Practice -- Outline of This Book -- A Survey of Some Basic Concepts Through Examples -- Payoff in a Simple Game -- Choosing Between Payoffs -- Future Lifetimes -- Simple and Compound Growth -- Classical Probability -- The Formal Language of Classical Probability -- Conditional Probability -- The Law of Total Probability -- Bayes' Theorem -- Appendix on Sets, Combinatorics, and Basic Probability Rules -- Random Variables and Probability Distributions -- Definitions and Basic Properties -- What Is a Random Variable? -- What Is a Probability Distribution? -- Types of Distributions -- Probability Mass Functions -- Probability Density Functions -- Mixed Distributions -- Equality and Equivalence of Random Variables -- Random Vectors and Bivariate Distributions -- Dependence and Independence of Random Variables -- The Law of Total Probability and Bayes' Theorem (Distributional Forms) -- Arithmetic Operations on Random Variables -- The Difference Between Sums and Mixtures -- Statistical Measures of Expectation, Variation, and Risk -- Expectation -- Deviation from Expectation -- Higher Moments -- Alternative Ways of Specifying Probability Distributions -- Moment and Cumulant Generating Functions -- Survival and Hazard Functions -- Appendix on Generalized Density Functions (Optional) -- Special Discrete Distributions.
520			_aThis textbook for a one-semester course in probability covers combinatorial probability theory based on sets and counting, random variables and probability distribution, special discrete and continuous distributions, and transformations of random variables. A separate chapter provides four extended examples that apply many of the key concepts. Anno
650			_aProbabilities.
942			_cREF
999			_c39932 _d39932