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Introduction to probability and its applications.

By: Contributor(s): Material type: TextTextPublisher: Pacific Grove, Calif. : London : Brooks/Cole ; Cengage Learning [distributor], [2010]Copyright date: ©2010Edition: Third edition / Richard L. Scheaffer, Linda YoungDescription: x, 470 p. : ill. ; 24 cmISBN:
  • 9780534386719
Subject(s): DDC classification:
  • 519.2 22
Contents:
1. Probability in the World Around Us -- Why Study Probability? -- Deterministic and Probabilistic Models -- Modeling Reality -- Deterministic Models -- Probabilistic Models -- Applications in Probability -- A Brief Historical Note -- A Look Ahead -- 2. Foundations of Probability -- Understanding Randomness: An Intuitive Notion of Probability -- Randomness with Known Structure -- Randomness with Unknown Structure -- Sampling a Finite Universe -- Sample Space and Events -- Definition of Probability -- Counting Rules Useful in Probability -- More Counting Rules Useful in Probability -- Summary -- 3. Conditional Probability and Independence -- Conditional Probability -- Independence -- Theorem of Total Probability and Bayes' Rule -- Odds, Odds Ratios, and Relative Risk -- Summary -- 4. Discrete Probability Distributions -- Random Variables and Their Probability Distributions -- Expected Values of Random Variables -- The Bernoulli Distribution -- The Binomial Distribution -- Probability Function -- Mean and Variance -- History and Applications -- The Geometric Distribution -- Probability Function -- Mean and Variance -- An Alternate Parameterization: Number of Trials Versus Number of Failures -- The Negative Binomial Distribution -- Probability Function -- Mean and Variance -- An Alternate Parameterization: Number of Trials Versus Number of Failures -- History and Applications -- The Poisson Distribution -- Probability Function -- Mean and Variance -- History and Applications -- The Hypergeometric Distribution -- The Probability Function -- Mean and Variance -- History and Applications -- The Moment-generating Function -- The Probability-generating Function -- Markov Chains -- Summary -- 5. Continuous Probability Distributions -- Continuous Random Variables and Their Probability Distributions -- Expected Values of Continuous Random Variables -- The Uniform Distribution -- Probability Density Function -- Mean and Variance -- History and Applications -- The Exponential Distribution -- Probability Density Function -- Mean and Variance -- Properties -- History and Applications -- The Gamma Distribution -- Probability Density Function -- Mean and Variance -- History and Applications -- The Normal Distribution -- The Normal Probability Density Function -- Mean and Variance -- Calculating Normal Probabilities -- Applications to Real Data -- Quantile-Quantile (Q-Q) Plots -- History -- The Beta Distribution -- Probability Density Function -- Mean and Variance -- H istory and Applications -- The Weibull Distribution -- Probability Density Function -- Mean and Variance -- History and Applications to Real Data -- Reliability -- Hazard Rate Function -- Series and Parallel Systems -- Redundancy -- Moment-generating Functions for Continuous Random Variables -- Expectations of Discontinuous Functions and Mixed Probability Distributions -- Summary -- 6. Multivariate Probability Distributions -- Bivariate and Marginal Probability Distributions -- Conditional Probability Distributions -- Independent Random Variables -- Expected Values of Functions of Random Variables -- Conditional Expectations -- The Multinomial Distribution -- More on the Moment-Generating Function -- Compounding and Its Applications -- Summary -- 7. Functions of Random Variables -- Introduction -- Functions of Discrete Random Variables -- Method of Distribution Functions -- Method of Transformations in One Dimension -- Method of Conditioning -- Method of Moment-Generating Functions -- Gamma Case -- Normal Case -- Normal and Gamma Relationships -- Method of Transformation - Two Dimensions -- Order Statistics -- Probability-Generating Functions: Applications to Random Sums of Random Variables -- Summary -- 8. Some Approximations To Probability Distributions: Limit Theorems -- Introduction -- Convergence in Probability -- Convergence in Distribution -- The Central Limit Theorem -- Combination of Convergence in Probability and Convergence in Distribution -- Summary -- 9. Extensions of Probability Theory -- The Poisson Process -- Birth and Death Processes: Biological Applications -- Queues: Engineering Applications -- Arrival Times for the Poisson Process -- Infinite Server Queue -- Renewal Theory: Reliability Applications -- Summary.
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Item type Current library Call number Copy number Status Date due Barcode Course reserves
Book City Campus City Campus Main Collection 519.2 SCH (Browse shelf(Opens below)) 1 Available A555214B
Book City Campus City Campus Main Collection 519.2 SCH (Browse shelf(Opens below)) 1 Available A555210B
SL 3 Day Loan City Campus City Campus Short Loan 3Day 519.2 SCH (Browse shelf(Opens below)) 1 Available A455662B

Applied Stochastic Models

Previous ed.: Belmont, Calif.: Duxbury, 1995.

"Advanced series"--cover.

Includes bibliographical references and index.

1. Probability in the World Around Us -- Why Study Probability? -- Deterministic and Probabilistic Models -- Modeling Reality -- Deterministic Models -- Probabilistic Models -- Applications in Probability -- A Brief Historical Note -- A Look Ahead -- 2. Foundations of Probability -- Understanding Randomness: An Intuitive Notion of Probability -- Randomness with Known Structure -- Randomness with Unknown Structure -- Sampling a Finite Universe -- Sample Space and Events -- Definition of Probability -- Counting Rules Useful in Probability -- More Counting Rules Useful in Probability -- Summary -- 3. Conditional Probability and Independence -- Conditional Probability -- Independence -- Theorem of Total Probability and Bayes' Rule -- Odds, Odds Ratios, and Relative Risk -- Summary -- 4. Discrete Probability Distributions -- Random Variables and Their Probability Distributions -- Expected Values of Random Variables -- The Bernoulli Distribution -- The Binomial Distribution -- Probability Function -- Mean and Variance -- History and Applications -- The Geometric Distribution -- Probability Function -- Mean and Variance -- An Alternate Parameterization: Number of Trials Versus Number of Failures -- The Negative Binomial Distribution -- Probability Function -- Mean and Variance -- An Alternate Parameterization: Number of Trials Versus Number of Failures -- History and Applications -- The Poisson Distribution -- Probability Function -- Mean and Variance -- History and Applications -- The Hypergeometric Distribution -- The Probability Function -- Mean and Variance -- History and Applications -- The Moment-generating Function -- The Probability-generating Function -- Markov Chains -- Summary -- 5. Continuous Probability Distributions -- Continuous Random Variables and Their Probability Distributions -- Expected Values of Continuous Random Variables -- The Uniform Distribution -- Probability Density Function -- Mean and Variance -- History and Applications -- The Exponential Distribution -- Probability Density Function -- Mean and Variance -- Properties -- History and Applications -- The Gamma Distribution -- Probability Density Function -- Mean and Variance -- History and Applications -- The Normal Distribution -- The Normal Probability Density Function -- Mean and Variance -- Calculating Normal Probabilities -- Applications to Real Data -- Quantile-Quantile (Q-Q) Plots -- History -- The Beta Distribution -- Probability Density Function -- Mean and Variance -- H istory and Applications -- The Weibull Distribution -- Probability Density Function -- Mean and Variance -- History and Applications to Real Data -- Reliability -- Hazard Rate Function -- Series and Parallel Systems -- Redundancy -- Moment-generating Functions for Continuous Random Variables -- Expectations of Discontinuous Functions and Mixed Probability Distributions -- Summary -- 6. Multivariate Probability Distributions -- Bivariate and Marginal Probability Distributions -- Conditional Probability Distributions -- Independent Random Variables -- Expected Values of Functions of Random Variables -- Conditional Expectations -- The Multinomial Distribution -- More on the Moment-Generating Function -- Compounding and Its Applications -- Summary -- 7. Functions of Random Variables -- Introduction -- Functions of Discrete Random Variables -- Method of Distribution Functions -- Method of Transformations in One Dimension -- Method of Conditioning -- Method of Moment-Generating Functions -- Gamma Case -- Normal Case -- Normal and Gamma Relationships -- Method of Transformation - Two Dimensions -- Order Statistics -- Probability-Generating Functions: Applications to Random Sums of Random Variables -- Summary -- 8. Some Approximations To Probability Distributions: Limit Theorems -- Introduction -- Convergence in Probability -- Convergence in Distribution -- The Central Limit Theorem -- Combination of Convergence in Probability and Convergence in Distribution -- Summary -- 9. Extensions of Probability Theory -- The Poisson Process -- Birth and Death Processes: Biological Applications -- Queues: Engineering Applications -- Arrival Times for the Poisson Process -- Infinite Server Queue -- Renewal Theory: Reliability Applications -- Summary.

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