A First Course in ProbabilityFor upper level or undergraduate/graduate level introduction to probability for math, science, engineering, and business students with a background in elementary calculus. This market-leading introduction to probability features exceptionally clear explanations of the mathematics of probability theory and explores its many diverse applications through numerous interesting and motivational examples. The outstanding problem sets and intuitive explanations are hallmark features of this market leading text. |
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Page 139
... variable X is said to be a Bernoulli random variable ( after the Swiss mathematician James Bernoulli ) if its probability mass function is given by Equa- tions ( 6.1 ) ... Binomial Random Variables The Bernoulli and Binomial Random Variables.
... variable X is said to be a Bernoulli random variable ( after the Swiss mathematician James Bernoulli ) if its probability mass function is given by Equa- tions ( 6.1 ) ... Binomial Random Variables The Bernoulli and Binomial Random Variables.
Page 519
... random variable , 139 , 186 Bernoulli trials , 117 Bertrand's paradox , 198–199 Best prize problem , 351-352 Beta distribution , 221-223 , 234 , 272-273 , 282 Binary symmetric channel , 450 Binomial coefficients , 8 Binomial random variable ...
... random variable , 139 , 186 Bernoulli trials , 117 Bertrand's paradox , 198–199 Best prize problem , 351-352 Beta distribution , 221-223 , 234 , 272-273 , 282 Binary symmetric channel , 450 Binomial coefficients , 8 Binomial random variable ...
Page 520
... random variables , 248-249 , 253,254 , 259-260 , 260-261 , 297 Independent increments , 432 Indicator random variable ... random variables , 277-278 , 282-283 Joint ... binomial random variable , 362-363 of a chi - squared random variable , 368 ...
... random variables , 248-249 , 253,254 , 259-260 , 260-261 , 297 Independent increments , 432 Indicator random variable ... random variables , 277-278 , 282-283 Joint ... binomial random variable , 362-363 of a chi - squared random variable , 368 ...
Contents
AXIOMS OF PROBABILITY | 24 |
CONDITIONAL PROBABILITY AND INDEPENDENCE | 64 |
RANDOM VARIABLES | 122 |
Copyright | |
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approximately assume binomial random variable black balls cards central limit theorem Chebyshev's inequality components compute conditional probability Consider continuous random variable Cov(X defined denote the event denote the number desired probability dice discrete random variable distribution function distribution with parameters equal Equation Example expected number expected value exponential random variable Find the probability flips follows function F Hence HINT independent trials inequality joint density function large numbers least Let X denote moment generating function nonnegative normal random variable normally distributed obtain occur P(EF P₁ P₂ pair percent permutation player Poisson random variable preceding prob probability density function probability mass function problem Proposition random number random vari randomly chosen result sample space sequence Show simulate Solution Let Suppose uniformly distributed Var(X variable with mean variable with parameters white balls wins X₁ Y₁ Y₂ Σ Σ