Basic probability theory with applications download

Author : Enders A. There is increasing awareness that we should ask not "Is it so? In particular, the student must have a good working knowledge of power series expan sions and integration.

Moreover, it would be helpful if the student has had some previous exposure to elementary probability theory, either in an elementary statistics course or a finite mathematics course in high school or college.

If these prerequisites are met, then a good part of the material in this book can be covered in a semester IS-week course that meets three hours a week. Readers will learn about the basic concepts of probability and its applications, preparing them for more advanced and specialized works.

Author : Henry C. Tuckwell Publisher: Routledge ISBN: Category: Mathematics Page: View: Read Now » This book provides a clear and straightforward introduction to applications of probability theory with examples given in the biological sciences and engineering.

The first chapter contains a summary of basic probability theory. Chapters two to five deal with random variables and their applications. Topics covered include geometric probability, estimation of animal and plant populations, reliability theory and computer simulation.

Chapter six contains a lucid account of the convergence of sequences of random variables, with emphasis on the central limit theorem and the weak law of numbers. The next four chapters introduce random processes, including random walks and Markov chains illustrated by examples in population genetics and population growth.

This edition also includes two chapters which introduce, in a manifestly readable fashion, the topic of stochastic differential equations and their applications. Book Summary: The book is an introduction to modern probability theory written by one of the famous experts in this area.

Readers will learn about the basic concepts of probability and its applications, preparing them for more advanced and specialized works.

Book Summary: Probability theory and its applications represent a discipline of fun damental importance to nearly all people working in the high-tech nology world that surrounds us. There is increasing awareness that we should ask not "Is it so?

This book is a text for a first course in the mathematical theory of probability for undergraduate students who have the prerequisite of at least two, and better three, semesters of calculus. In particular, the student must have a good working knowledge of power series expan sions and integration. Moreover, it would be helpful if the student has had some previous exposure to elementary probability theory, either in an elementary statistics course or a finite mathematics course in high school or college.

If these prerequisites are met, then a good part of the material in this book can be covered in a semester IS-week course that meets three hours a week. Book Summary: Introductory Probability is a pleasure to read and provides a fine answer to the question: How do you construct Brownian motion from scratch, given that you are a competent analyst?

There are at least two ways to develop probability theory. The more familiar path is to treat it as its own discipline, and work from intuitive examples such as coin flips and conundrums such as the Monty Hall problem. An alternative is to first develop measure theory and analysis, and then add interpretation. Bhattacharya and Waymire take the second path.

Book Summary: This updated and revised first-course textbook in applied probability provides a contemporary and lively post-calculus introduction to the subject of probability. The exposition reflects a desirable balance between fundamental theory and many applications involving a broad range of real problem scenarios. It is intended to appeal to a wide audience, including mathematics and statistics majors, prospective engineers and scientists, and those business and social science majors interested in the quantitative aspects of their disciplines.

The textbook contains enough material for a year-long course, though many instructors will use it for a single term one semester or one quarter. A one-term course would cover material in the core chapters , supplemented by selections from one or more of the remaining chapters on statistical inference Ch. For a year-long course, core chapters are accessible to those who have taken a year of univariate differential and integral calculus; matrix algebra, multivariate calculus, and engineering mathematics are needed for the latter, more advanced chapters.

Book Summary: This text develops the necessary background in probability theory underlying diverse treatments of stochastic processes and their wide-ranging applications. In this second edition, the text has been reorganized for didactic purposes, new exercises have been added and basic theory has been expanded.

General Markov dependent sequences and their convergence to equilibrium is the subject of an entirely new chapter. The introduction of conditional expectation and conditional probability very early in the text maintains the pedagogic innovation of the first edition; conditional expectation is illustrated in detail in the context of an expanded treatment of martingales, the Markov property, and the strong Markov property.

Weak convergence of probabilities on metric spaces and Brownian motion are two topics to highlight. This also includes a treatment of the Berry—Esseen error estimate in the central limit theorem. The authors assume mathematical maturity at a graduate level; otherwise the book is suitable for students with varying levels of background in analysis and measure theory. For the reader who needs refreshers, theorems from analysis and measure theory used in the main text are provided in comprehensive appendices, along with their proofs, for ease of reference.

Both authors have co-authored numerous books, including a series of four upcoming graduate textbooks in stochastic processes with applications.

Book Summary: This book provides engineers with focused treatment of the mathematics needed to understand probability, random variables, and stochastic processes, which are essential mathematical disciplines used in communications engineering. The author explains the basic concepts of these topics as plainly as possible so that people with no in-depth knowledge of these mathematical topics can better appreciate their applications in real problems. Applications examples are drawn from various areas of communications.

Book Summary: This is an introduction to probabilistic and statistical concepts necessary to understand the basic ideas and methods of stochastic differential equations. Based on measure theory, which is introduced as smoothly as possible, it provides practical skills in the use of MAPLE in the context of probability and its applications.

It offers to graduates and advanced undergraduates an overview and intuitive background for more advanced studies. Book Summary: This text contains ample material for a one term precalculus introduction to probability theory. Although the development of the subject is rigorous, experimental motivation is maintained throughout the text.

Also, statistical and practical applications are given throughout. The core of the text consists of the unstarred sections, most of chapters and Included are finite probability spaces, com binatorics, set theory, independence and conditional probability, random variables, Chebyshev's theorem, the law of large numbers, the binomial distribution, the normal distribution and the normal approxi mation to the binomial distribution.

The starred sections include limiting and infinite processes, a mathematical discussion of symmetry, and game theory. I have, in most places throughout the text, given decimal equivalents to fractional answers.

This textbook can be used by undergraduate students in pure and applied sciences such as mathematics, engineering, computer science, finance and economics. Skip to main content Skip to table of contents.

Advertisement Hide. This service is more advanced with JavaScript available. Basic Probability Theory with Applications. Authors view affiliations Mario Lefebvre. Presents elementary probability theory with interesting and well-chosen applications that illustrate the theory Main results in elementary probability, random variables, random vectors and the central limit theorem are covered Applications in reliability theory, basic queuing models, and time series are presented Over exercises reinforce the material and provide students with ample practice Introductory chapter reviews the basic elements of differential calculus which are used in the material to follow Includes supplementary material: sn.

Front Matter Pages Review of differential calculus. Pages