Probability and Mathematical Statistics

Probability and Mathematical Statistics
Author: Eugene Lukacs
Publisher: Academic Press
Total Pages: 255
Release: 2014-05-10
Genre: Mathematics
ISBN: 1483269205

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.

Probability and Mathematical Statistics

Probability and Mathematical Statistics
Author: Mary C. Meyer
Publisher: SIAM
Total Pages: 720
Release: 2019-06-24
Genre: Mathematics
ISBN: 1611975786

This book develops the theory of probability and mathematical statistics with the goal of analyzing real-world data. Throughout the text, the R package is used to compute probabilities, check analytically computed answers, simulate probability distributions, illustrate answers with appropriate graphics, and help students develop intuition surrounding probability and statistics. Examples, demonstrations, and exercises in the R programming language serve to reinforce ideas and facilitate understanding and confidence. The book’s Chapter Highlights provide a summary of key concepts, while the examples utilizing R within the chapters are instructive and practical. Exercises that focus on real-world applications without sacrificing mathematical rigor are included, along with more than 200 figures that help clarify both concepts and applications. In addition, the book features two helpful appendices: annotated solutions to 700 exercises and a Review of Useful Math. Written for use in applied masters classes, Probability and Mathematical Statistics: Theory, Applications, and Practice in R is also suitable for advanced undergraduates and for self-study by applied mathematicians and statisticians and qualitatively inclined engineers and scientists.

40 Puzzles and Problems in Probability and Mathematical Statistics

40 Puzzles and Problems in Probability and Mathematical Statistics
Author: Wolf Schwarz
Publisher: Springer Science & Business Media
Total Pages: 124
Release: 2007-11-25
Genre: Mathematics
ISBN: 0387735127

This book is based on the view that cognitive skills are best acquired by solving challenging, non-standard probability problems. Many puzzles and problems presented here are either new within a problem solving context (although as topics in fundamental research they are long known) or are variations of classical problems which follow directly from elementary concepts. A small number of particularly instructive problems is taken from previous sources which in this case are generally given. This book will be a handy resource for professors looking for problems to assign, for undergraduate math students, and for a more general audience of amateur scientists.

Introduction to Probability and Mathematical Statistics

Introduction to Probability and Mathematical Statistics
Author: Lee J. Bain
Publisher: Duxbury Press
Total Pages: 644
Release: 2000-03-01
Genre: Mathematics
ISBN: 9780534380205

The Second Edition of INTRODUCTION TO PROBABILITY AND MATHEMATICAL STATISTICS focuses on developing the skills to build probability (stochastic) models. Lee J. Bain and Max Engelhardt focus on the mathematical development of the subject, with examples and exercises oriented toward applications.

Advances in Probability and Mathematical Statistics

Advances in Probability and Mathematical Statistics
Author: Daniel Hernández‐Hernández
Publisher: Springer Nature
Total Pages: 178
Release: 2021-11-14
Genre: Mathematics
ISBN: 303085325X

This volume contains papers which were presented at the XV Latin American Congress of Probability and Mathematical Statistics (CLAPEM) in December 2019 in Mérida-Yucatán, México. They represent well the wide set of topics on probability and statistics that was covered at this congress, and their high quality and variety illustrates the rich academic program of the conference.

Probability Theory

Probability Theory
Author: L. E. Maistrov
Publisher: Academic Press
Total Pages: 296
Release: 2014-07-03
Genre: Mathematics
ISBN: 1483218635

Probability Theory: A Historical Sketch covers the probability theory, mainly axiomatization problems. The book discusses the prehistory of the probability theory; the first stage in the development of probability theory; and the development of probability theory to the middle of the 19th century. The text also describes the probability theory in the second half of the 19th century; and the axiomatic foundations of the probability theory. Historians and mathematicians will find the book invaluable.

Fourier Analysis in Probability Theory

Fourier Analysis in Probability Theory
Author: Tatsuo Kawata
Publisher: Academic Press
Total Pages: 681
Release: 2014-06-17
Genre: Mathematics
ISBN: 148321852X

Fourier Analysis in Probability Theory provides useful results from the theories of Fourier series, Fourier transforms, Laplace transforms, and other related studies. This 14-chapter work highlights the clarification of the interactions and analogies among these theories. Chapters 1 to 8 present the elements of classical Fourier analysis, in the context of their applications to probability theory. Chapters 9 to 14 are devoted to basic results from the theory of characteristic functions of probability distributors, the convergence of distribution functions in terms of characteristic functions, and series of independent random variables. This book will be of value to mathematicians, engineers, teachers, and students.

Geometric Aspects of Probability Theory and Mathematical Statistics

Geometric Aspects of Probability Theory and Mathematical Statistics
Author: V.V. Buldygin
Publisher: Springer Science & Business Media
Total Pages: 314
Release: 2013-06-29
Genre: Mathematics
ISBN: 9401716870

It is well known that contemporary mathematics includes many disci plines. Among them the most important are: set theory, algebra, topology, geometry, functional analysis, probability theory, the theory of differential equations and some others. Furthermore, every mathematical discipline consists of several large sections in which specific problems are investigated and the corresponding technique is developed. For example, in general topology we have the following extensive chap ters: the theory of compact extensions of topological spaces, the theory of continuous mappings, cardinal-valued characteristics of topological spaces, the theory of set-valued (multi-valued) mappings, etc. Modern algebra is featured by the following domains: linear algebra, group theory, the theory of rings, universal algebras, lattice theory, category theory, and so on. Concerning modern probability theory, we can easily see that the clas sification of its domains is much more extensive: measure theory on ab stract spaces, Borel and cylindrical measures in infinite-dimensional vector spaces, classical limit theorems, ergodic theory, general stochastic processes, Markov processes, stochastical equations, mathematical statistics, informa tion theory and many others.

All of Statistics

All of Statistics
Author: Larry Wasserman
Publisher: Springer Science & Business Media
Total Pages: 446
Release: 2013-12-11
Genre: Mathematics
ISBN: 0387217363

Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.