Probability: The Science of Uncertainty with Applications to Investments, Insurance, and EngineeringBrooks/Cole, 2001 - 448 páginas This 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 |
Índice
Introduction | 1 |
A Survey of Some Basic Concepts Through Examples | 19 |
Classical Probability | 57 |
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Términos y frases comunes
actuarial present value approximation arithmetic expectation assumed average beta beta distribution calculate Chapter coin Consequently consider continuous random variable contract cumulant generating function deductible defined derived desired probability Determine the distribution Determine the probability discrete random variable discussed distribution function distribution with parameters example expected value exponential distribution Figure function Fx future lifetime Fx(x fy(y gamma distribution geometric expectation given graph Hence identically distributed inequality integration interpretation investment large number law of total loss mixture moment generating function Mx(t negative binomial distribution normal distribution number of claims outcome particular payoff payout Poisson distribution policyholder portfolio Pr(E Pr(F Pr(X premium probability density function probability distribution probability mass function Px(x quantity relative frequency risk risk-free S₁ sample space skewness standard deviation standard normal Suppose survival function Sx(x theorem tosses total probability transformation Var(X Weibull distribution X₁ X₂ Y₁