FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
|
|
- Title Basic Probability Theory
- Author(s) Robert B. Ash
- Publisher: Dover Publications (June 26, 2008)
- Hardcover/Paperback 352 pages
- eBook PDF Files, and a single PDF (78 MB)
- Language: English
- ISBN-10/ASIN: 0486466280
- ISBN-13: 978-0486466286
- Share This:
This introduction to more advanced courses in probability and real analysis emphasizes the probabilistic way of thinking, rather than measure-theoretic concepts.
Geared toward advanced undergraduates and graduate students, its sole prerequisite is calculus, this introductory text surveys random variables, conditional probability and expectation, characteristic functions, infinite sequences of random variables, Markov chains, and an introduction to statistics. Complete solutions to some of the problems appear at the end of the book.
Taking statistics as its major field of application, the text opens with a review of basic concepts, advancing to surveys of random variables, the properties of expectation, conditional probability and expectation, and characteristic functions. Subsequent topics include infinite sequences of random variables, Markov chains, and an introduction to statistics. Complete solutions to some of the problems appear at the end of the book.
About the Authors- Robert B. Ash is Professor Emeritus of Mathematics at the University of Illinois.
- Probability, Stochastic Process, Queueing Theory, etc.
- Statistics and SAS Programming
- Financial Mathematics and Engineering
- Computational and Algorithmic Mathematics
- Combinatorics and Game Theory
-
Introduction to Probability for Data Science (Stanley Chan)
This book is an introductory textbook in undergraduate probability in the context of data science to emphasize the inseparability between data (computing) and probability (theory) in our time, with examples in both MATLAB and Python.
-
Introduction to Probability, Statistics, and Random Processes
This book introduces students to probability, statistics, and stochastic processes. It provides a clear and intuitive approach to these topics while maintaining mathematical accuracy. The book contains a large number of solved exercises.
-
Foundations of Constructive Probability Theory (Yuen-Kwok Chan)
This book provides a systematic and general theory of probability within the framework of Constructive Mathematics. It can serve as a parallel introduction into constructive mathematics and rigorous probability theory.
-
Introduction to Random Matrices: Theory and Practice
This is a book for absolute beginners. The aim is to provide a truly accessible introductory account of Random Matrix theory. Most chapters are accompanied by MATLAB codes to guide readers through the numerical check of most analytical results.
-
Elementary Probability for Applications (Rick Durrett)
This clear and lively introduction to probability theory concentrates on the results that are the most useful for applications, including combinatorial probability and Markov chains. Concise and focused, it is designed for students familiar with basic calculus.
-
Essentials of Stochastic Processes (Rick Durrett)
Stochastic processes have become important for many fields, including mathematical finance and engineering. Written by one of the worlds leading probabilists, this book presents recent results previously available only in specialized monographs.
-
Probability in Electrical Engineering and Computer Science
This textbook motivates and illustrates the techniques of applied probability by applications in electrical engineering and computer science (EECS). Python labs enable the readers to experiment and consolidate their understanding.
-
Introduction to Probability (Charles M. Grinstead, et al)
The book is a beautiful introduction to probability theory at the beginning level. The book contains a lot of examples and an easy development of theory without any sacrifice of rigor, keeping the abstraction to a minimal level.
-
Probability Theory: The Logic of Science (E. T. Jaynes)
Going beyond the conventional mathematics of probability theory, this study views the subject in a wider context. It discusses new results, along with applications of probability theory to a variety of problems.
-
Probability: Theory and Examples (Rick Durrett)
This lively introduction to measure-theoretic probability theory covers laws of large numbers, central limit theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion.
-
Probability Theory and Stochastic Processes with Applications
This book provides an introduction to probability theory and discrete and continuous stochastic processes and its applications. It has a unique approach that provides a broad and wide introduction into the fascinating area of probability theory.
-
Probabilistic Machine Learning: An Introduction (Kevin Murphy)
This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. It is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.
:
|
|