A First Course in ProbabilityThis market leader is written as an elementary introduction to the mathematical theory of probability for readers in mathematics, engineering, and the sciences who possess the prerequisite knowledge of elementary calculus. A major thrust of the Fifth Edition has been to make the book more accessible to today's readers. The exercise sets have been revised to include more simple, "mechanical" problems and new section of Self-test Problems, with fully worked out solutions, conclude each chapter. In addition many new applications have been added to demonstrate the importance of probability in real situations. A software diskette, packaged with each copy of the book, provides an easy to use tool to derive probabilities for binomial, Poisson, and normal random variables. It also illustrates and explores the central limit theorem, works with the strong law of large numbers, and more. |
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assume ball number binomial random variable black balls Chebyshev's inequality conditional probability continuous random variable defined denote the event denote the number desired probability dice Distributed Random Variables distribution with parameters equal Equation Example 3a expected number exponential random variable exponentially distributed fair coin Find the probability flips follows Hence identically distributed independent and identically independent random variables independent trials inequality joint density function joint probability Jointly Distributed Random large numbers law of large Let X denote moment generating function n₁ normal random variable normally distributed obtain occur P(EF P₁ P₂ percent player Poisson random variable Probability and Independence probability density function probability mass function problem Proposition prove Random Variables Ch result sample space Show Solution Suppose Theoretical Exercises uniformly distributed urn contains variable with parameters variance white balls wins X₁ Y₁