Probability and Mathematical Statistics
Probability and Mathematical Statistics

Author: Eugene Lukacs

Publisher: Academic Press

Published: 2014-05-10

Total Pages: 255

ISBN-13: 1483269205

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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.

Probability and Mathematical Statistics: Theory, Applications, and Practice in R
Probability and Mathematical Statistics: Theory, Applications, and Practice in R

Author: Mary C. Meyer

Publisher: SIAM

Published: 2019-06-24

Total Pages: 707

ISBN-13: 1611975786

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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

Published: 2007-11-25

Total Pages: 124

ISBN-13: 0387735127

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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.

Advances in Probability and Mathematical Statistics
Advances in Probability and Mathematical Statistics

Author: Daniel Hernández‐Hernández

Publisher: Springer Nature

Published: 2021-11-14

Total Pages: 178

ISBN-13: 303085325X

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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.

Introduction to Probability and Mathematical Statistics
Introduction to Probability and Mathematical Statistics

Author: Lee J. Bain

Publisher: Duxbury Press

Published: 2000-03-01

Total Pages: 644

ISBN-13: 9780534380205

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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.

Probability, Statistics, and Truth
Probability, Statistics, and Truth

Author: Richard Von Mises

Publisher: Courier Corporation

Published: 1981-01-01

Total Pages: 273

ISBN-13: 0486242145

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This comprehensive study of probability considers the approaches of Pascal, Laplace, Poisson, and others. It also discusses Laws of Large Numbers, the theory of errors, and other relevant topics.

Probability Theory
Probability Theory

Author: L. E. Maistrov

Publisher: Academic Press

Published: 2014-07-03

Total Pages: 296

ISBN-13: 1483218635

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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.

Mathematical Statistics
Mathematical Statistics

Author: Thomas S. Ferguson

Publisher: Academic Press

Published: 2014-07-10

Total Pages: 409

ISBN-13: 1483221237

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Mathematical Statistics: A Decision Theoretic Approach presents an investigation of the extent to which problems of mathematical statistics may be treated by decision theory approach. This book deals with statistical theory that could be justified from a decision-theoretic viewpoint. Organized into seven chapters, this book begins with an overview of the elements of decision theory that are similar to those of the theory of games. This text then examines the main theorems of decision theory that involve two more notions, namely the admissibility of a decision rule and the completeness of a class of decision rules. Other chapters consider the development of theorems in decision theory that are valid in general situations. This book discusses as well the invariance principle that involves groups of transformations over the three spaces around which decision theory is built. The final chapter deals with sequential decision problems. This book is a valuable resource for first-year graduate students in mathematics.