## An introduction to probability theory and mathematical statistics

**Author**: V. K. Rohatgi

**Publisher:**John Wiley & Sons Inc

**ISBN:**N.A

**Category:**Mathematics

**Page:**684

**View:**9907

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Probability; Random variables and their probability distributions; Moments and generating functions; Random vectors; Some special distributions; Limit theorems; Sample moments and their distributions; The theory of point estimation; Neyman-Pearson theory of testing of hypotheses; Some further results on hypotheses testing; Confidence estimation; The general linear hypothesis; Nonparametric statistical inference; Sequential statistical inference.

## An Introduction to Probability Theory and Its Applications

**Author**: William Feller

**Publisher:**John Wiley & Sons

**ISBN:**N.A

**Category:**Mathematics

**Page:**704

**View:**8593

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The exponential and the uniform densities; Special densities. Randomization; Densities in higher dimensions. Normal densities and processes; Probability measures and spaces; Probability distributions in Rr; A survey of some important distributions and processes; Laws of large numbers. Aplications in analysis; The basic limit theorems; Infinitely divisible distributions and semi-groups; Markov processes and semi-groups; Renewal theory; Random walks in R1; Laplace transforms. Tauberian theorems. Resolvents; Aplications of Laplace transforms; Characteristic functions; Expansions related to the central limit theorem; Infinitely divisible distributions; Applications of Fourier methods to ramdom walks; harmonic analysis; Answers to problems.

## An Introduction to Probability and Statistics

**Author**: Vijay K. Rohatgi,A.K. Md. Ehsanes Saleh

**Publisher:**John Wiley & Sons

**ISBN:**111879964X

**Category:**Mathematics

**Page:**688

**View:**8717

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This Third Edition provides a solid and well-balancedintroduction to probability theory and mathematicalstatistics. The book is divided into three parts: Chapters1-6 form the core of probability fundamentals and foundations;Chapters 7-11 cover statistics inference; and the remainingchapters focus on special topics. For course sequences thatseparate probability and mathematics statistics, the first part ofthe book can be used for a course in probability theory, followedby a course in mathematical statistics based on the second part,and possibly, one or more chapters on special topics. Thebook contains over 550 problems, 350 worked-out examples, and 200side notes for reader reference. Numerous figures have beenadded to illustrate examples and proofs, and answers to selectproblems are now included. Many parts of the book haveundergone substantial rewriting, and the book has also beenreorganized. Chapters 6 and 7 have been interchanged to emphasizethe role of asymptotics in statistics, and the new Chapter 7contains all of the needed basic material on asymptotics. Chapter 6 also includes new material on resampling, specificallybootstrap. The new Further Results chapter include someestimation procedures such as M-estimatesand bootstrapping. A new chapter on regression analysishas also been added and contains sections on linear regression,multiple regression, subset regression, logistic regression, andPoisson regression.

## An introduction to probability theory and its applications

**Author**: William Feller

**Publisher:**John Wiley & Sons

**ISBN:**9780471257080

**Category:**Mathematics

**Page:**528

**View:**1000

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Major changes in this edition include the substitution of probabilistic arguments for combinatorial artifices, and the addition of new sections on branching processes, Markov chains, and the De Moivre-Laplace theorem.

## Probability and Mathematical Statistics

*An Introduction*

**Author**: Eugene Lukacs

**Publisher:**Academic Press

**ISBN:**1483269205

**Category:**Mathematics

**Page:**254

**View:**5590

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Probability and Mathematical Statistics: An Introduction provides a well-balanced first introduction to probability theory and mathematical statistics. This book is organized into two sections encompassing nine chapters. The first part deals with the concept and elementary properties of probability space, and random variables and their probability distributions. This part also considers the principles of limit theorems, the distribution of random variables, and the so-called student’s distribution. The second part explores pertinent topics in mathematical statistics, including the concept of sampling, estimation, and hypotheses testing. This book is intended primarily for undergraduate statistics students.

## Introduction to Probability Theory and Statistical Inference

**Author**: Harold J. Larson

**Publisher:**N.A

**ISBN:**N.A

**Category:**Mathematics

**Page:**430

**View:**6045

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Discusses probability theory and to many methods used in problems of statistical inference. The Third Edition features material on descriptive statistics. Cramer-Rao bounds for variance of estimators, two-sample inference procedures, bivariate normal probability law, F-Distribution, and the analysis of variance and non-parametric procedures. Contains numerous practical examples and exercises.

## Probability Theory

*A First Course in Probability Theory and Statistics*

**Author**: Werner Linde

**Publisher:**Walter de Gruyter GmbH & Co KG

**ISBN:**3110466198

**Category:**Mathematics

**Page:**409

**View:**580

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This book is intended as an introduction to Probability Theory and Mathematical Statistics for students in mathematics, the physical sciences, engineering, and related fields. It is based on the author’s 25 years of experience teaching probability and is squarely aimed at helping students overcome common difficulties in learning the subject. The focus of the book is an explanation of the theory, mainly by the use of many examples. Whenever possible, proofs of stated results are provided. All sections conclude with a short list of problems. The book also includes several optional sections on more advanced topics. This textbook would be ideal for use in a first course in Probability Theory. Contents: Probabilities Conditional Probabilities and Independence Random Variables and Their Distribution Operations on Random Variables Expected Value, Variance, and Covariance Normally Distributed Random Vectors Limit Theorems Mathematical Statistics Appendix Bibliography Index

## An Introduction to Probability Theory

**Author**: K. Itô

**Publisher:**Cambridge University Press

**ISBN:**9780521269605

**Category:**Mathematics

**Page:**213

**View:**6788

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One of the most distinguished probability theorists in the world rigorously explains the basic probabilistic concepts while fostering an intuitive understanding of random phenomena.

## Introduction to Probability with Statistical Applications

**Author**: Géza Schay

**Publisher:**Birkhäuser

**ISBN:**3319306200

**Category:**Mathematics

**Page:**385

**View:**9468

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Now in its second edition, this textbook serves as an introduction to probability and statistics for non-mathematics majors who do not need the exhaustive detail and mathematical depth provided in more comprehensive treatments of the subject. The presentation covers the mathematical laws of random phenomena, including discrete and continuous random variables, expectation and variance, and common probability distributions such as the binomial, Poisson, and normal distributions. More classical examples such as Montmort's problem, the ballot problem, and Bertrand’s paradox are now included, along with applications such as the Maxwell-Boltzmann and Bose-Einstein distributions in physics. Key features in new edition: * 35 new exercises * Expanded section on the algebra of sets * Expanded chapters on probabilities to include more classical examples * New section on regression * Online instructors' manual containing solutions to all exercises“/p> Advanced undergraduate and graduate students in computer science, engineering, and other natural and social sciences with only a basic background in calculus will benefit from this introductory text balancing theory with applications. Review of the first edition: This textbook is a classical and well-written introduction to probability theory and statistics. ... the book is written ‘for an audience such as computer science students, whose mathematical background is not very strong and who do not need the detail and mathematical depth of similar books written for mathematics or statistics majors.’ ... Each new concept is clearly explained and is followed by many detailed examples. ... numerous examples of calculations are given and proofs are well-detailed." (Sophie Lemaire, Mathematical Reviews, Issue 2008 m)

## Introduction to Probability and Mathematical Statistics

**Author**: Václav Fabian

**Publisher:**John Wiley & Sons Incorporated

**ISBN:**N.A

**Category:**Mathematics

**Page:**466

**View:**1243

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## Mathematical Statistics with Applications in R

**Author**: Kandethody M. Ramachandran,Chris P. Tsokos

**Publisher:**Elsevier

**ISBN:**012417132X

**Category:**Mathematics

**Page:**826

**View:**3300

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Mathematical Statistics with Applications in R, Second Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applications. The book covers many modern statistical computational and simulation concepts that are not covered in other texts, such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. By combining the discussion on the theory of statistics with a wealth of real-world applications, the book helps students to approach statistical problem solving in a logical manner. This book provides a step-by-step procedure to solve real problems, making the topic more accessible. It includes goodness of fit methods to identify the probability distribution that characterizes the probabilistic behavior or a given set of data. Exercises as well as practical, real-world chapter projects are included, and each chapter has an optional section on using Minitab, SPSS and SAS commands. The text also boasts a wide array of coverage of ANOVA, nonparametric, MCMC, Bayesian and empirical methods; solutions to selected problems; data sets; and an image bank for students. Advanced undergraduate and graduate students taking a one or two semester mathematical statistics course will find this book extremely useful in their studies. Step-by-step procedure to solve real problems, making the topic more accessible Exercises blend theory and modern applications Practical, real-world chapter projects Provides an optional section in each chapter on using Minitab, SPSS and SAS commands Wide array of coverage of ANOVA, Nonparametric, MCMC, Bayesian and empirical methods

## Stochastik

*Einführung in die Wahrscheinlichkeitstheorie und Statistik*

**Author**: Hans-Otto Georgii

**Publisher:**Walter de Gruyter GmbH & Co KG

**ISBN:**3110359707

**Category:**Mathematics

**Page:**448

**View:**7137

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Dieses Lehrbuch gibt eine Einführung in die "Mathematik des Zufalls", bestehend aus den beiden Teilbereichen Wahrscheinlichkeitstheorie und Statistik. Die stochastischen Konzepte, Modelle und Methoden werden durch typische Anwendungsbeispiele motiviert und anschließend systematisch entwickelt. Der dafür notwendige maßtheoretische Rahmen wird gleich zu Beginn auf elementarem Niveau bereitgestellt. Zahlreiche Übungsaufgaben, zum Teil mit Lösungsskizzen, illustrieren und ergänzen den Text. Zielgruppe sind Studierende der Mathematik ab dem dritten Semester, sowie Naturwissenschaftler und Informatiker mit Interesse an den mathematischen Grundlagen der Stochastik. Die 5. Auflage wurde nochmals bearbeitet und maßvoll ergänzt.

## Elementare Wahrscheinlichkeitstheorie und stochastische Prozesse

**Author**: Kai L. Chung

**Publisher:**Springer-Verlag

**ISBN:**3642670334

**Category:**Mathematics

**Page:**346

**View:**1847

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Aus den Besprechungen: "Unter den zahlreichen Einführungen in die Wahrscheinlichkeitsrechnung bildet dieses Buch eine erfreuliche Ausnahme. Der Stil einer lebendigen Vorlesung ist über Niederschrift und Übersetzung hinweg erhalten geblieben. In jedes Kapitel wird sehr anschaulich eingeführt. Sinn und Nützlichkeit der mathematischen Formulierungen werden den Lesern nahegebracht. Die wichtigsten Zusammenhänge sind als mathematische Sätze klar formuliert." #FREQUENZ#1

## An Introduction to Probability and Statistical Inference

**Author**: George G. Roussas

**Publisher:**Academic Press

**ISBN:**0128004371

**Category:**Mathematics

**Page:**624

**View:**3871

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An Introduction to Probability and Statistical Inference, Second Edition, guides you through probability models and statistical methods and helps you to think critically about various concepts. Written by award-winning author George Roussas, this book introduces readers with no prior knowledge in probability or statistics to a thinking process to help them obtain the best solution to a posed question or situation. It provides a plethora of examples for each topic discussed, giving the reader more experience in applying statistical methods to different situations. This text contains an enhanced number of exercises and graphical illustrations where appropriate to motivate the reader and demonstrate the applicability of probability and statistical inference in a great variety of human activities. Reorganized material is included in the statistical portion of the book to ensure continuity and enhance understanding. Each section includes relevant proofs where appropriate, followed by exercises with useful clues to their solutions. Furthermore, there are brief answers to even-numbered exercises at the back of the book and detailed solutions to all exercises are available to instructors in an Answers Manual. This text will appeal to advanced undergraduate and graduate students, as well as researchers and practitioners in engineering, business, social sciences or agriculture. Content, examples, an enhanced number of exercises, and graphical illustrations where appropriate to motivate the reader and demonstrate the applicability of probability and statistical inference in a great variety of human activities Reorganized material in the statistical portion of the book to ensure continuity and enhance understanding A relatively rigorous, yet accessible and always within the prescribed prerequisites, mathematical discussion of probability theory and statistical inference important to students in a broad variety of disciplines Relevant proofs where appropriate in each section, followed by exercises with useful clues to their solutions Brief answers to even-numbered exercises at the back of the book and detailed solutions to all exercises available to instructors in an Answers Manual

## Measure Theory and Probability Theory

**Author**: Krishna B. Athreya,Soumendra N. Lahiri

**Publisher:**Springer Science & Business Media

**ISBN:**038732903X

**Category:**Business & Economics

**Page:**618

**View:**9231

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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. Krishna B. Athreya is a professor at the departments of mathematics and statistics and a Distinguished Professor in the College of Liberal Arts and Sciences at the Iowa State University. He has been a faculty member at University of Wisconsin, Madison; Indian Institute of Science, Bangalore; Cornell University; and has held visiting appointments in Scandinavia and Australia. He is a fellow of the Institute of Mathematical Statistics USA; a fellow of the Indian Academy of Sciences, Bangalore; an elected member of the International Statistical Institute; and serves on the editorial board of several journals in probability and statistics. Soumendra N. Lahiri is a professor at the department of statistics at the Iowa State University. He is a fellow of the Institute of Mathematical Statistics, a fellow of the American Statistical Association, and an elected member of the International Statistical Institute.

## Wahrscheinlichkeitsrechnung und mathematische Statistik

**Author**: Marek Fisz

**Publisher:**N.A

**ISBN:**N.A

**Category:**Mathematical statistics

**Page:**777

**View:**8162

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## Introduction to Probability with R

**Author**: Kenneth Baclawski

**Publisher:**CRC Press

**ISBN:**9781420065220

**Category:**Mathematics

**Page:**384

**View:**5563

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Based on a popular course taught by the late Gian-Carlo Rota of MIT, with many new topics covered as well, Introduction to Probability with R presents R programs and animations to provide an intuitive yet rigorous understanding of how to model natural phenomena from a probabilistic point of view. Although the R programs are small in length, they are just as sophisticated and powerful as longer programs in other languages. This brevity makes it easy for students to become proficient in R. This calculus-based introduction organizes the material around key themes. One of the most important themes centers on viewing probability as a way to look at the world, helping students think and reason probabilistically. The text also shows how to combine and link stochastic processes to form more complex processes that are better models of natural phenomena. In addition, it presents a unified treatment of transforms, such as Laplace, Fourier, and z; the foundations of fundamental stochastic processes using entropy and information; and an introduction to Markov chains from various viewpoints. Each chapter includes a short biographical note about a contributor to probability theory, exercises, and selected answers. The book has an accompanying website with more information.

## Probability Theory, Random Processes and Mathematical Statistics

**Author**: Y. Rozanov

**Publisher:**Springer Science & Business Media

**ISBN:**9401104492

**Category:**Mathematics

**Page:**259

**View:**9207

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Probability Theory, Theory of Random Processes and Mathematical Statistics are important areas of modern mathematics and its applications. They develop rigorous models for a proper treatment for various 'random' phenomena which we encounter in the real world. They provide us with numerous tools for an analysis, prediction and, ultimately, control of random phenomena. Statistics itself helps with choice of a proper mathematical model (e.g., by estimation of unknown parameters) on the basis of statistical data collected by observations. This volume is intended to be a concise textbook for a graduate level course, with carefully selected topics representing the most important areas of modern Probability, Random Processes and Statistics. The first part (Ch. 1-3) can serve as a self-contained, elementary introduction to Probability, Random Processes and Statistics. It contains a number of relatively sim ple and typical examples of random phenomena which allow a natural introduction of general structures and methods. Only knowledge of elements of real/complex analysis, linear algebra and ordinary differential equations is required here. The second part (Ch. 4-6) provides a foundation of Stochastic Analysis, gives information on basic models of random processes and tools to study them. Here a familiarity with elements of functional analysis is necessary. Our intention to make this course fast-moving made it necessary to present important material in a form of examples.

## An Introduction to Probability and Mathematical Statistics

**Author**: Howard G. Tucker

**Publisher:**Academic Press

**ISBN:**1483225143

**Category:**Mathematics

**Page:**240

**View:**7847

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An Introduction to Probability and Mathematical Statistics provides information pertinent to the fundamental aspects of probability and mathematical statistics. This book covers a variety of topics, including random variables, probability distributions, discrete distributions, and point estimation. Organized into 13 chapters, this book begins with an overview of the definition of function. This text then examines the notion of conditional or relative probability. Other chapters consider Cochran's theorem, which is of extreme importance in that part of statistical inference known as analysis of variance. This book discusses as well the fundamental principles of testing statistical hypotheses by providing the reader with an idea of the basic problem and its relation to practice. The final chapter deals with the problem of estimation and the Neyman theory of confidence intervals. This book is a valuable resource for undergraduate university students who are majoring in mathematics. Students who are majoring in physics and who are inclined toward abstract mathematics will also find this book useful.

## An introduction to mathematical statistics

**Author**: F. Bijma,M. Jonker,A. van der Vaart

**Publisher:**Amsterdam University Press

**ISBN:**9048536111

**Category:**Mathematics

**Page:**N.A

**View:**2843

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Statistics is the science that focuses on drawing conclusions from data, by modeling and analyzing the data using probabilistic models. In An Introduction to Mathematical Statistics the authors describe key concepts from statistics and give a mathematical basis for important statistical methods. Much attention is paid to the sound application of those methods to data. The three main topics in statistics are estimators, tests, and confidence regions. The authors illustrate these in many examples, with a separate chapter on regression models, including linear regression and analysis of variance. They also discuss the optimality of estimators and tests, as well as the selection of the best-fitting model. Each chapter ends with a case study in which the described statistical methods are applied. This book assumes a basic knowledge of probability theory, calculus, and linear algebra.