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E-grāmata: Probability: A Lively Introduction

(Vrije Universiteit, Amsterdam)
  • Formāts: PDF+DRM
  • Izdošanas datums: 06-Oct-2017
  • Izdevniecība: Cambridge University Press
  • Valoda: eng
  • ISBN-13: 9781108314138
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  • Formāts: PDF+DRM
  • Izdošanas datums: 06-Oct-2017
  • Izdevniecība: Cambridge University Press
  • Valoda: eng
  • ISBN-13: 9781108314138
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Providing a comprehensive, yet concise introduction to probability, this textbook is the go-to guide for undergraduate and first-year-graduate-level students in mathematics and statistics. With engaging real life examples and more than 750 interesting problems, it gives students confidence in their own problem-solving skills.

Probability has applications in many areas of modern science, not to mention in our daily life. Its importance as a mathematical discipline cannot be overrated, and it is a fascinating and surprising topic in its own right. This engaging textbook with its easy-to-follow writing style provides a comprehensive yet concise introduction to the subject. It covers all of the standard material for undergraduate and first-year-graduate-level courses as well as many topics that are usually not found in standard texts, such as Bayesian inference, Markov chain Monte Carlo simulation, and Chernoff bounds.

Recenzijas

'This is an attractive textbook for an introductory probability course at the upper undergraduate level. It covers not only the standard material for such a course (discrete probability, the axioms of probability, conditional probability, discrete and continuous random variables, jointly distributed random variables, limit theorems, Markov chains, etc.) but also some topics that might be considered more unusual, such as Kelly betting, renewal-reward stochastic processes, and the law of iterated logarithms. Topics from statistics (confidence intervals, Student-t distribution, Baysian inference, etc.) also appear. The book is quite well-written, nicely motivated, demonstrates considerable enthusiasm for the material, and gives lots of examples of the usefulness of probability. Mark Hunacek, MAA Reviews As with its predecessor, Probability: A Lively Introduction has an engaging and sympathetic tone which will be welcomed by those wrestling with this endlessly fascinating but tricky subject. Robert A. J. Matthews, Significance 'This text serves as an excellent introduction to probability theory. Tijms has achieved the difficult feat of writing a book that is useful as both a textbook and a reference resource. As he wisely points out in the introduction, a key step in attracting students' attention to this field is providing clear, natural examples. In this book, every chapter is full of such examples. Besides covering the topics expected in an entry-level book, the author also covers multivariate normal distributions and the chi-square test, generating functions, and Markov chains (both the discrete time and the continuous time cases). Many students will appreciate the four appendixes at the end of the book. The first three contain the necessary background in enumerative combinatorics, set theory, and calculus, and make the book even more widely accessible in doing so. The fourth appendix introduces a more advanced concept, Monte Carlo simulations. There are plenty of excellent exercises in each chapter, half of which come with detailed solutions (not just numerical answers).' M. Bona, Choice 'In this book, Henk Tijms aims at sharing his passion and enthusiasm for the fascinating world of probability with his readers. I can only say that he convincingly succeeded to do so!' Ivo Adan, European Journal of Operational Research 'A most interesting aspect of this text is its exposition. The text relies heavily on a narrative approach: graphics and lengthy displayed calculations are infrequent. Happily, the author writes well, with an obvious enthusiasm (the 'liveliness' of the title is correct) and a gift for choosing appropriate and revealing examples. Often these examples provide jumping-o points for further discussion or exploration. The material is also presented accurately and at an appropriate level of rigor. One oddity is that theorems are not presented in the classic boxed-o fashion followed by a clearly marked proof. Instead, theorems (which the author calls 'rules') are stated and proofs or sketches of proofs are given within the narrative. The text also features an abundance of interesting exercises, ranging from elementary to challenging. Full solutions to the odd-numbered problems appear at the end of the book, and the publisher oers a password-protected site with solutions to all the exercises in the text.' Thomas Polaski, Mathematical Reviews 'This is indeed a lively introduction to probability theory. The book is addressed basically to undergraduate students and their teachers. All traditional notions, results, ideas and techniques are included and discussed in detail. This is done smoothly and gradually in a master style.' Jordan M. Stoyanov, ZB Math Reviews

Papildus informācija

Comprehensive, yet concise, this textbook is the go-to guide to learn why probability is so important and its applications.
Preface ix
1 Foundations of Probability Theory
1(41)
1.1 Probabilistic Foundations
3(4)
1.2 Classical Probability Model
7(8)
1.3 Geometric Probability Model
15(4)
1.4 Compound Chance Experiments
19(6)
1.5 Some Basic Rules
25(11)
1.6 Inclusion-Exclusion Rule
36(6)
2 Conditional Probability
42(43)
2.1 Concept of Conditional Probability
42(5)
2.2 Chain Rule for Conditional Probabilities
47(7)
2.3 Law of Conditional Probability
54(13)
2.4 Bayes' Rule in Odds Form
67(10)
2.5 Bayesian Inference - Discrete Case
77(8)
3 Discrete Random Variables
85(61)
3.1 Concept of a Random Variable
85(4)
3.2 Expected Value
89(10)
3.3 Expected Value of Sums of Random Variables
99(7)
3.4 Substitution Rule and Variance
106(7)
3.5 Independence of Random Variables
113(5)
3.6 Binomial Distribution
118(6)
3.7 Poisson Distribution
124(11)
3.8 Hypergeometric Distribution
135(5)
3.9 Other Discrete Distributions
140(6)
4 Continuous Random Variables
146(63)
4.1 Concept of Probability Density
147(9)
4.2 Expected Value of a Continuous Random Variable
156(4)
4.3 Substitution Rule and the Variance
160(4)
4.4 Uniform and Triangular Distributions
164(3)
4.5 Exponential Distribution
167(10)
4.6 Gamma, Weibull, and Beta Distributions
177(3)
4.7 Normal Distribution
180(13)
4.8 Other Continuous Distributions
193(5)
4.9 Inverse-Transformation Method and Simulation
198(4)
4.10 Failure-Rate Function
202(3)
4.11 Probability Distributions and Entropy
205(4)
5 Jointly Distributed Random Variables
209(30)
5.1 Joint Probability Mass Function
209(3)
5.2 Joint Probability Density Function
212(7)
5.3 Marginal Probability Densities
219(9)
5.4 Transformation of Random Variables
228(5)
5.5 Covariance and Correlation Coefficient
233(6)
6 Multivariate Normal Distribution
239(22)
6.1 Bivariate Normal Distribution
239(9)
6.2 Multivariate Normal Distribution
248(2)
6.3 Multidimensional Central Limit Theorem
250(7)
6.4 Chi-Square Test
257(4)
7 Conditioning by Random Variables
261(41)
7.1 Conditional Distributions
262(7)
7.2 Law of Conditional Probability for Random Variables
269(7)
7.3 Law of Conditional Expectation
276(7)
7.4 Conditional Expectation as a Computational Tool
283(11)
7.5 Bayesian Inference - Continuous Case
294(8)
8 Generating Functions
302(19)
8.1 Generating Functions
302(9)
8.2 Branching Processes and Generating Functions
311(2)
8.3 Moment-Generating Functions
313(5)
8.4 Central Limit Theorem Revisited
318(3)
9 Additional Topics in Probability
321(27)
9.1 Bounds and Inequalities
321(6)
9.2 Strong Law of Large Numbers
327(8)
9.3 Kelly Betting System
335(4)
9.4 Renewal-Reward Processes
339(9)
10 Discrete-Time Markov Chains
348(55)
10.1 Markov Chain Model
349(8)
10.2 Time-Dependent Analysis of Markov Chains
357(5)
10.3 Absorbing Markov Chains
362(11)
10.4 Long-Run Analysis of Markov Chains
373(13)
10.5 Markov Chain Monte Carlo Simulation
386(17)
11 Continuous-Time Markov Chains
403(35)
11.1 Markov Chain Model
403(11)
11.2 Time-Dependent Probabilities
414(6)
11.3 Limiting Probabilities
420(18)
Appendix A Counting Methods 438(5)
Appendix B Basics of Set Theory 443(4)
Appendix C Some Basic Results from Calculus 447(4)
Appendix D Basics of Monte Carlo Simulation 451(12)
Answers to Odd-Numbered Problems 463(69)
Index 532
Henk Tijms is emeritus professor at the Vrije University, Amsterdam. He is the author of several textbooks and numerous papers on applied probability and stochastic optimization. In 2008, Henk Tijms received the prestigious INFORMS Expository Writing Award for his publications and books. His activities also include the popularization of probability to high school students and the general public; he also regularly contributed to the Numberplay blog of the New York Times with probability puzzles.