3 edition of introduction to Stein"s method found in the catalog.
introduction to Stein"s method
|Statement||A D Barbour, Louis H Y Chen.|
|The Physical Object|
|Pagination||xii, 225 p. :|
|Number of Pages||225|
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: An Introduction to Stein's Method (Lecture Notes Series, Institute for Mathematical Sciences, National University of Singapore) (): Barbour, Andrew, Chen, Louis Hsiao Yun: BooksCited by: A Short Introduction to Stein’s Method Gesine Reinert Department of Statistics University of Oxford Michaelmas Term Gesine ReinertDepartment of Statistics University of Oxford A Short Introduction to Stein’s Method.
Lecture 1: Normal approximation Lecture 2: Poisson approximation and other distributions. An introduction to Stein's method A. Barbour, Louis H.
Chen A common theme in probability theory is the approximation of complicated probability distributions by simpler ones, the central limit theorem being a classical example. The course will cover the fundamentals of Stein's method, starting with the Poisson and Normal distributions to illustrate the construction of the Stein equation and the derivation of the properties required on its solution.
A number of coupling methods for use in the Stein equation will be presented, as well as it use in cases of local dependence. Math - Introduction to Stein's Method The course will cover the fundamentals of Stein's method, starting with the Normal and Poisson distributions to illustrate the construction of the Stein equation and the derivation of the properties required on its solution.
Lecture Notes Series, Institute for Mathematical Sciences, National University of Singapore An Introduction to Stein's Method, pp. () No Access Three general approaches to Stein's method Gesine Reinert. An introduction to Stein's method. Lecture Notes Series, Institute for Mathematical Sciences, National University of Singapore.
Singapore University Press. ISBN X. CS1 maint: multiple names: authors list CS1 maint: extra text: authors list ; A standard reference is the book by Stein, Stein.
System Upgrade on Fri, Jun 26th, at 5pm (ET) During this period, our website will be offline for less than an hour but the E-commerce and registration of new. 1 Introduction In this lecture, we will cover a brief introduction of Stein’s method which is used as a general tool for proving central limit theorems.
We have so far derived lower bounds for property testing and estimation by characterizing the gener. Stein’s method for comparison of univariate distributions Ley, Christophe, Reinert, Gesine, and Swan, Yvik, Probability Surveys, ; Deformation Quantization in the Teaching of Lie Group Representations Balsomo, Alexander J.
and Nable, Job A., Journal of Geometry and Symmetry in Physics, ; Exponential functionals of Brownian motion, I: Probability laws at fixed time Matsumoto, Hiroyuki. Abstract. Methodological aspects of the Stein method are exceptionally well discussed in the literature, and for more advanced applications the reader is advised to consult the books and papers referenced at the end of this chapter.
Stein's method is a tool which makes this possible in a wide variety of situations. Traditional approaches, for example using Fourier analysis, become awkward to carry through in situations in which dependence plays an important part, whereas Stein's method can often still be applied to great effect.
Abstract: Stein's method is a powerful technique for proving central limit theorems in probability theory when more straightforward approaches cannot be implemented easily. This article begins with a survey of the historical development of Stein's method and some recent advances.
This is followed by a description of a "general purpose" variant of Stein's method that may be called the. Get this from a library.
An introduction to Stein's method. [A D Barbour; Louis H Y Chen;] -- "This volume of lecture notes provides a detailed introduction to the theory and application of Stein's method, in a form suitable for graduate students who want to acquaint themselves with the.
Search the world's most comprehensive index of full-text books. My library. ISBN: X X OCLC Number: Notes: Literaturangaben. Description: XII, Seiten. Contents: Stein's Method for Normal Approximation (L Chen & Q-M Shao); Stein's Method for Poisson and Compound Poisson Approximation (T Erhardsson); Stein's Method and Poisson Process Approximation (A Xia); Three General Approaches to Stein's Method.
The first paper, by Charles Stein himself, associates exchangeable pairs with Stein's method. But it is not introductory to the method itself. The second paper, by Persi Diaconis, uses Stein's method to derive convergence rates for some Markov chains.
The introduction states "Charles Stein has introduced a general approach to proving Reviews: 1. An introduction to Stein‘s method. Abstract. A common theme in probability theory is the approximation of complicated probability distributions by simpler ones, the central limit theorem being a classical example.
Stein's method is a tool which makes this possible in a wide variety of situations. Traditional approaches, for example. The papers may not be as rewarding for those who only want to get an understanding of what Stein estimators are all about, in spite of the title of the book.
The many proofs in the papers distract from the getting an introduction to the Stein method. This survey article discusses the main concepts and techniques of Stein's method for distributional approximation by the normal, Poisson, exponential, and geometric distributions, and also its relation to concentration inequalities.
The material is presented at a level accessible to beginning graduate students studying probability with the main emphasis on the themes that are common to. Contents Preface xiii I Foundations Introduction 3 1 The Role of Algorithms in Computing 5 Algorithms 5 Algorithms as a technology 11 2 Getting Started 16 Insertion sort 16 Analyzing algorithms 23 Designing algorithms 29 3 Growth of Functions 43 Asymptotic notation 43 Standard notations and common functions 53 4 Divide-and-Conquer 65 The maximum-subarray .A Short Introduction to Stein’s Method Gesine Reinert Department of Statistics University of Oxford 1.
Overview Lecture 1: focusses mainly on normal approximation Lecture 2: other approximations 2. 1. The need for bounds Distributional approximations: Example X 1,X.Charles’ book (stein, ) upon his suggestion.
A little history: Stein’s method of exchangeable pairs and characterizing operators, not to be confused with shrinkage, was ﬁrst used by Charles in the early 70’s, at the 6th Berkeley Symposium to prove central limit theorems for dependent random variables (Stein.