Lýsing:
This textbook is an introduction to probability theory using measure theory. It is designed for graduate students in a variety of fields (mathematics, statistics, economics, management, finance, computer science, and engineering) who require a working knowledge of probability theory that is mathematically precise, but without excessive technicalities. The text provides complete proofs of all the essential introductory results.
Nevertheless, the treatment is focused and accessible, with the measure theory and mathematical details presented in terms of intuitive probabilistic concepts, rather than as separate, imposing subjects. In this new edition, many exercises and small additional topics have been added and existing ones expanded. The text strikes an appropriate balance, rigorously developing probability theory while avoiding unnecessary detail.
Annað
- Höfundur: Jeffrey S Rosenthal
- Útgáfa:2
- Útgáfudagur: 2006-11-14
- Hægt að prenta út 2 bls.
- Hægt að afrita 2 bls.
- Format:Page Fidelity
- ISBN 13: 9789813101654
- Print ISBN: 9789812703705
- ISBN 10: 9813101652
Efnisyfirlit
- Contents
- Preface to the First Edition
- Preface to the Second Edition
- 1 The need for measure theory
- 1.1 Various kinds of random variables
- 1.2 The uniform distribution and non-measurable sets
- 1.3 Exercises
- 1.4 Section summary
- 2 Probability triples
- 2.1 Basic definition
- 2.2 Constructing probability triples
- 2.3 The Extension Theorem
- 2.4 Constructing the Uniform[0 1] distribution
- 2.5 Extensions of the Extension Theorem
- 2.6 Coin tossing and other measures
- 2.7 Exercises
- 2.8 Section summary
- 3 Further probabilistic foundations
- 3.1 Random variables
- 3.2 Independence
- 3.3 Continuity of probabilities
- 3.4 Limit events
- 3.5 Tail fields
- 3.6 Exercises
- 3.7 Section summary
- 4 Expected values
- 4.1 Simple random variables
- 4.2 General non-negative random variables
- 4.3 Arbitrary random variables
- 4.4 The integration connection
- 4.5 Exercises
- 4.6 Section summary
- 5 Inequalities and convergence
- 5.1 Various inequalities
- 5.2 Convergence of random variables
- 5.3 Laws of large numbers
- 5.4 Eliminating the moment conditions
- 5.5 Exercises
- 5.6 Section summary
- 6 Distributions of random variables
- 6.1 Change of variable theorem
- 6.2 Examples of distributions
- 6.3 Exercises
- 6.4 Section summary
- 7 Stochastic processes and gambling games
- 7.1 A first existence theorem
- 7.2 Gambling and gambler's ruin
- 7.3 Gambling policies
- 7.4 Exercises
- 7.5 Section summary
- 8 Discrete Markov chains
- 8.1 A Markov chain existence theorem
- 8.2 Transience recurrence and irreducibility
- 8.3 Stationary distributions and convergence
- 8.4 Existence of stationary distributions
- 8.5 Exercises
- 8.6 Section summary
- 9 More probability theorems
- 9.1 Limit theorems
- 9.2 Differentiation of expectation
- 9.3 Moment generating functions and large deviations
- 9.4 Fubini's Theorem and convolution
- 9.5 Exercises
- 9.6 Section summary
- 10 Weak convergence
- 10.1 Equivalences of weak convergence
- 10.2 Connections to other convergence
- 10.3 Exercises
- 10.4 Section summary
- 11 Characteristic functions
- 11.1 The continuity theorem
- 11.2 The Central Limit Theorem
- 11.3 Generalisations of the Central Limit Theorem
- 11.4 Method of moments
- 11.5 Exercises
- 11.6 Section summary
- 12 Decomposition of probability laws
- 12.1 Lebesgue and Hahn decompositions
- 12.2 Decomposition with general measures
- 12.3 Exercises
- 12.4 Section summary
- 13 Conditional probability and expectation
- 13.1 Conditioning on a random variable
- 13.2 Conditioning on a sub-o-algebra
- 13.3 Conditional variance
- 13.4 Exercises
- 13.5 Section summary
- 14 Martingales
- 14.1 Stopping times
- 14.2 Martingale convergence
- 14.3 Maximal inequality
- 14.4 Exercises
- 14.5 Section summary
- 15 General stochastic processes
- 15.1 Kolmogorov Existence Theorem
- 15.2 Markov chains on general state spaces
- 15.3 Continuous-time Markov processes
- 15.4 Brownian motion as a limit
- 15.5 Existence of Brownian motion
- 15.6 Diffusions and stochastic integrals
- 15.7 Ito's Lemma
- 15.8 The Black-Scholes equation
- 15.9 Section summary
- A. Mathematical Background
- A.1 Sets and functions
- A.2 Countable sets
- A.3 Epsilons and Limits
- A.4 Infimums and supremums
- A.5 Equivalence relations
- B. Bibliography
- B.1 Background in real analysis
- B.2 Undergraduate-level probability
- B.3 Graduate-level probability
- B.4 Pure measure theory
- B.5 Stochastic processes
- B.6 Mathematical finance
- Index
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- Gerð : 208
- Höfundur : 16356
- Útgáfuár : 2006