Probability, statistics, and stochastic processes /
This book provides a unique and€balanced approach to probability, statistics, and stochastic processes. € Readers gain a solid foundation in all three fields that serves as a stepping stone to more advanced investigations into each area.€ The Second Edition features new coverage of analysis of varia...
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| Other Authors: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hoboken :
John Wiley & Sons,
2012.
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| Edition: | 2nd ed. |
| Subjects: | |
| Online Access: | CONNECT |
MARC
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| 100 | 1 | |a Olofsson, Peter, |d 1963- | |
| 245 | 1 | 0 | |a Probability, statistics, and stochastic processes / |c Peter Olofsson, Mikael Andersson. |
| 250 | |a 2nd ed. | ||
| 260 | |a Hoboken : |b John Wiley & Sons, |c 2012. | ||
| 300 | |a 1 online resource (574 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references (pages 551-552) and index. | ||
| 505 | 0 | |a PROBABILITY, STATISTICS, AND STOCHASTIC PROCESSES; CONTENTS; Preface; Preface to the First Edition; 1 Basic Probability Theory; 1.1 Introduction; 1.2 Sample Spaces and Events; 1.3 The Axioms of Probability; 1.4 Finite Sample Spaces and Combinatorics; 1.4.1 Combinatorics; 1.5 Conditional Probability and Independence; 1.5.1 Independent Events; 1.6 The Law of Total Probability and Bayes' Formula; 1.6.1 Bayes' Formula; 1.6.2 Genetics and Probability; 1.6.3 Recursive Methods; Problems; 2 Random Variables; 2.1 Introduction; 2.2 Discrete Random Variables; 2.3 Continuous Random Variables. | |
| 505 | 8 | |a 2.3.1 The Uniform Distribution2.3.2 Functions of Random Variables; 2.4 Expected Value and Variance; 2.4.1 The Expected Value of a Function of a Random Variable; 2.4.2 Variance of a Random Variable; 2.5 Special Discrete Distributions; 2.5.1 Indicators; 2.5.2 The Binomial Distribution; 2.5.3 The Geometric Distribution; 2.5.4 The Poisson Distribution; 2.5.5 The Hypergeometric Distribution; 2.5.6 Describing Data Sets; 2.6 The Exponential Distribution; 2.7 The Normal Distribution; 2.8 Other Distributions; 2.8.1 The Lognormal Distribution; 2.8.2 The Gamma Distribution; 2.8.3 The Cauchy Distribution. | |
| 505 | 8 | |a 2.8.4 Mixed Distributions2.9 Location Parameters; 2.10 The Failure Rate Function; 2.10.1 Uniqueness of the Failure Rate Function; Problems; 3 Joint Distributions; 3.1 Introduction; 3.2 The Joint Distribution Function; 3.3 Discrete Random Vectors; 3.4 Jointly Continuous Random Vectors; 3.5 Conditional Distributions and Independence; 3.5.1 Independent Random Variables; 3.6 Functions of Random Vectors; 3.6.1 Real-Valued Functions of Random Vectors; 3.6.2 The Expected Value and Variance of a Sum; 3.6.3 Vector-Valued Functions of Random Vectors; 3.7 Conditional Expectation. | |
| 505 | 8 | |a 3.7.1 Conditional Expectation as a Random Variable3.7.2 Conditional Expectation and Prediction; 3.7.3 Conditional Variance; 3.7.4 Recursive Methods; 3.8 Covariance and Correlation; 3.8.1 The Correlation Coefficient; 3.9 The Bivariate Normal Distribution; 3.10 Multidimensional Random Vectors; 3.10.1 Order Statistics; 3.10.2 Reliability Theory; 3.10.3 The Multinomial Distribution; 3.10.4 The Multivariate Normal Distribution; 3.10.5 Convolution; 3.11 Generating Functions; 3.11.1 The Probability Generating Function; 3.11.2 The Moment Generating Function; 3.12 The Poisson Process. | |
| 505 | 8 | |a 3.12.1 Thinning and SuperpositionProblems; 4 Limit Theorems; 4.1 Introduction; 4.2 The Law of Large Numbers; 4.3 The Central Limit Theorem; 4.3.1 The Delta Method; 4.4 Convergence in Distribution; 4.4.1 Discrete Limits; 4.4.2 Continuous Limits; Problems; 5 Simulation; 5.1 Introduction; 5.2 Random Number Generation; 5.3 Simulation of Discrete Distributions; 5.4 Simulation of Continuous Distributions; 5.5 Miscellaneous; Problems; 6 Statistical Inference; 6.1 Introduction; 6.2 Point Estimators; 6.2.1 Estimating the Variance; 6.3 Confidence Intervals. | |
| 500 | |a 6.3.1 Confidence Interval for the Mean in the Normal Distribution with Known Variance. | ||
| 520 | |a This book provides a unique and€balanced approach to probability, statistics, and stochastic processes. € Readers gain a solid foundation in all three fields that serves as a stepping stone to more advanced investigations into each area.€ The Second Edition features new coverage of analysis of variance (ANOVA), consistency and efficiency of estimators, asymptotic theory for maximum likelihood estimators, empirical distribution function and the Kolmogorov-Smirnov test, general linear models, multiple comparisons, Markov chain Monte Carlo (MCMC), Brownian motion, martingales, and. | ||
| 588 | 0 | |a Print version record. | |
| 590 | |a O'Reilly Online Learning Platform: Academic Edition (SAML SSO Access) | ||
| 650 | 0 | |a Stochastic processes |v Textbooks. | |
| 650 | 0 | |a Probabilities |v Textbooks. | |
| 650 | 0 | |a Mathematical statistics |v Textbooks. | |
| 655 | 7 | |a Textbooks. |2 fast |0 (OCoLC)fst01423863 | |
| 700 | 1 | |a Andersson, Mikael, |d 1963- | |
| 730 | 0 | |a WORLDSHARE SUB RECORDS | |
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