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Lectures on Numerical Methods in Bifurcation Problems
Methods for Finding Zeros in Polynomials
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Educational Psychology by Edward L. Thorndike
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Linear Algebra: Theorems and Applications
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A Short Biographical Dictionary of English Literature
Lectures on Sieve Methods and Prime Number Theory
Dollars and Sense by William Crosbie Hunter
The Theory of the Theatre by Clayton Hamilton
The Mathematics of Investment
Occupiers of Wall Street: Losers or Game Changers
The Solution of the Pyramid Problem
Lectures on Moduli of Curves
Walden by Henry David Thoreau
Methods for Finding Zeros in Polynomials
Lectures on Stochastic Flows and Applications
Educational Psychology by Edward L. Thorndike
The Last Days of Tolstoy by V. G. Chertkov
Globalization and Responsibility
Lectures on Siegel Modular Forms and Representation by Quadratic Forms
Lectures on Topics In One-Parameter Bifurcation Problems
History of the Incas by Pedro Sarmiento de Gamboa
Linear Algebra: Theorems and Applications
Lectures on Stochastic Differential Equations and Malliavin Calculus
A Short Biographical Dictionary of English Literature
Lectures on Sieve Methods and Prime Number Theory
Dollars and Sense by William Crosbie Hunter
The Theory of the Theatre by Clayton Hamilton
The Mathematics of Investment
Occupiers of Wall Street: Losers or Game Changers
The Solution of the Pyramid Problem
Lectures on Moduli of Curves
Walden by Henry David Thoreau
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Measure Theory and Probability Theory (Springer Texts in Statistics) (repost)
Posted on 2010-06-28
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More Measure Theory and Probability Theory (Springer Texts in Statistics) This is a graduate level textbook on measure theory and probability theory. The book can be used as a text for a two semester sequence of courses in measure theory and probability theory, with an option to include supplemental material on stochastic processes and special topics. It is intended primarily for first year Ph.D. students in mathematics and statistics although mathematically advanced students from engineering and economics would also find the book useful. Prerequisites are kept to the minimal level of an understanding of basic real analysis concepts such as limits, continuity, differentiability, Riemann integration, and convergence of sequences and series. A review of this material is included in the appendix. The book starts with an informal introduction that provides some heuristics into the abstract concepts of measure and integration theory, which are then rigorously developed. The first part of the book can be used for a standard real analysis course for both mathematics and statistics Ph.D. students as it provides full coverage of topics such as the construction of Lebesgue-Stieltjes measures on real line and Euclidean spaces, the basic convergence theorems, L^p spaces, signed measures, Radon-Nikodym theorem, Lebesgue's decomposition theorem and the fundamental theorem of Lebesgue integration on R, product spaces and product measures, and Fubini-Tonelli theorems. It also provides an elementary introduction to Banach and Hilbert spaces, convolutions, Fourier series and Fourier and Plancherel transforms. Thus part I would be particularly useful for students in a typical Statistics Ph.D. program if a separate course on real analysis is not a standard requirement. Part II (chapters 6-13) provides full coverage of standard graduate level probability theory. It starts with Kolmogorov's probability model and Kolmogorov's existence theorem. It then treats thoroughly the laws of large numbers including renewal theory and ergodic theorems with applications and then weak convergence of probability distributions, characteristic functions, the Levy-Cramer continuity theorem and the central limit theorem as well as stable laws. It ends with conditional expectations and conditional probability, and an introduction to the theory of discrete time martingales. Part III (chapters 14-18) provides a modest coverage of discrete time Markov chains with countable and general state spaces, MCMC, continuous time discrete space jump Markov processes, Brownian motion, mixing sequences, bootstrap methods, and branching processes. It could be used for a topics/seminar course or as an introduction to stochastic processes. From the reviews: "...There are interesting and non-standard topics that are not usually included in a first course in measture-theoretic probability including Markov Chains and MCMC, the bootstrap, limit theorems for martingales and mixing sequences, Brownian motion and Markov processes. The material is well-suported with many end-of-chapter problems." D.L. McLeish for Short Book Reviews of the ISI, December 2006 MY LINKS OTHER PUBLICATIONS
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