The standard rules of probability can be interpreted as uniquely valid principles in logic. In this book, E. T. Jaynes dispels the imaginary distinction between 'probability theory' and 'statistical inference', leaving a logical unity and simplicity, which provides greater technical power and flexibility in applications. This book goes beyond the conventional mathematics of probability theory, viewing the subject in a wider context. New results are discussed, along with applications of probability theory to a wide variety of problems in physics, mathematics, economics, chemistry and biology. It contains many exercises and problems, and is suitable for use as a textbook on graduate level courses involving data analysis. The material is aimed at readers who are already familiar with applied mathematics at an advanced undergraduate level or higher. The book will be of interest to scientists working in any area where inference from incomplete information is necessary.

Assuming only an elementary background in discrete mathematics, this textbook is an excellent introduction to the probabilistic techniques and paradigms used in the development of probabilistic algorithms and analyses. It includes random sampling, expectations, Markov's and Chevyshev's inequalities, Chernoff bounds, balls and bins models, the probabilistic method, Markov chains, MCMC, martingales, entropy, and other topics. The book is designed to accompany a one- or two-semester course for graduate students in computer science and applied mathematics.

This classic introduction to probability theory for beginning graduate students covers laws of large numbers, central limit theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion. It is a comprehensive treatment concentrating on the results that are the most useful for applications. Its philosophy is that the best way to learn probability is to see it in action, so there are 200 examples and 450 problems. The new edition begins with a short chapter on measure theory to orient readers new to the subject.

《应用随机过程概率模型导论》是一部经典的随机过程著作, 叙述深入浅出、涉及面广，主要内容有随机变量、条件概率及条件期望、离散及连续马尔可夫链、指数分布、泊松过程、布朗运动及平稳过程、更新理论及排队论等；也包括了随机过程在物理、生物、运筹、网络、遗传、经济、保险、金融及可靠性中的应用，特别是有关随机模拟的内容, 给随机系统运行的模拟计算提供了有力的工具。《应用随机过程概率模型导论》有约700道习题, 其中带星号的习题还提供了解答。

Berenson shows readers how statistics is used in each functional area of business. Basic Business Statistics shows the relevance of statistics by familiarizing readers with the statistical applications used in the business world, providing clear instructions for using statistical applications, and offering ample opportunities for practice. The twelfth edition has built on the application emphasis and provides enhanced coverage of statistics.

The theory of probability is a powerful tool that helps electrical and computer engineers to explain, model, analyze, and design the technology they develop. The text begins at the advanced undergraduate level, assuming only a modest knowledge of probability, and progresses through more complex topics mastered at graduate level. The first five chapters cover the basics of probability and both discrete and continuous random variables. The later chapters have a more specialized coverage, including random vectors, Gaussian random vectors, random processes, Markov Chains, and convergence. Describing tools and results that are used extensively in the field, this is more than a textbook; it is also a reference for researchers working in communications, signal processing, and computer network traffic analysis. With over 300 worked examples, some 800 homework problems, and sections for exam preparation, this is an essential companion for advanced undergraduate and graduate students. Further resources for this title, including solutions (for Instructors only), are available online at www.cambridge.org/9780521864701.

Many probability books are written by mathematicians and have the built in bias that the reader is assumed to be a mathematician coming to the material for its beauty. This textbook is geared towards beginning graduate students from a variety of disciplines whose primary focus is not necessarily mathematics for its own sake. Instead, A Probability Path is designed for those requiring a deep understanding of advanced probability for their research in statistics, applied probability, biology, operations research, mathematical finance, and engineering.

《概率、随机变量与随机过程》是美国著名学者A·帕普里斯教授所著的一本经典教材。自1965年第1版问世以来至今已第4版，一直被美国多所大学用作相关专业的研究生教材。它的特点是将高深的理论恰当地应用于工程实际，因而深受工程界专业人士的青睐。本书（第4版）在保持前三版风格和精华的基础上作了大量的修订：更新了约三分之一的章节内容，包括几个新的专题和新增的第15、16章，增加了大量的新例子，进一步澄清了一些复杂的概念，使读者能更容易地理解它们。
本书可供无线电通信系统、信号处理、控制理论、优化、滤波等专业的研究生和本科高年级学生使用，也可供相关领域的科开人员和工程技术人员参考。

《初等概率论(第4版)(英文版)》是一部介绍概率论及其应用的入门教程。其原始版本面世已经有30余年，但仍然是本科一二年级的经典概率教程。在第4版中增加了两章讲述应用和数学金融。传承前面版本详细、严谨的风格，讲述了有价证券和期货理论的基本知识。书中用最初等的方法讲述了概率测度、随机变量、分布以及期望等基本概念。离散和连续的案例都有所涉及，在讲述后者的时候运用了微积分知识。配以大量的典型例子重点讲述概率推理，集中介绍了组合问题、Poison过程、随机漫步、遗传模型和Markov链。每章末都附有习题及其解答。

《概率论及其应用》(第3版)涉及面极广，不仅讨论了概率论在离散空间中的诸多课题，也涉及了概率论在物理学、化学、生物学(特别是遗传学)、博弈论及经济学等方面的应用，主要内容有：样本空间及其上的概率计算，独立随机变量之和的随机起伏，事件的组合及条件概率，离散随机变量及其数字特征，大数定律，离散的马尔可夫过程及其各种重要特征，更新理论等，除正文外，《概率论及其应用》(第3版)还附有六七百道习题和大量的附录。

《概率论教程:英文版(第3版)》是一本享誉世界的经典概率论教材，令众多读者受益无穷。自出版以来。已被世界75％以上的大学的数万名学生使用。《概率论教程:英文版(第3版)》内容丰富，逻辑清晰，叙述严谨。不仅可以拓展读者的视野。而且还将为其后续的学习和研究打下坚实基础。此外，《概率论教程:英文版(第3版)》的习题较多，都经过细心的遴选，从易到难，便于读者巩固练习。本版补充了有关测度和积分方面的内容，并增加了一些习题。

《概率导论(第2版)》是在MIT开设概率论入门课程的基础上编写的, 其内容全面, 例题和习题丰富, 结构层次性强, 能够满足不同读者的需求。书中介绍了概率模型、离散随机变量和连续随机变量、多元随机变量以及极限理论等概率论基本知识, 还介绍了矩母函数、条件概率的现代定义、独立随机变量的和、最小二乘估计等高级内容。
《概率导论(第2版)》可作为所有高等院校概率论入门的基础教程, 也可作为有关概率论方面的参考书。

Review
“This is the third edition of a popular textbook on stochastic processes. It is intended for advanced undergraduates and beginning graduate students and aimed at an intermediate level between an undergraduate course in probability and the first graduate course that uses measure theory.” (William J. Satzer, MAA Reviews, maa.org, February, 2017)
From the Back Cover
Building upon the previous editions, this textbook is a first course in stochastic processes taken by undergraduate and graduate students (MS and PhD students from math, statistics, economics, computer science, engineering, and finance departments) who have had a course in probability theory. It covers Markov chains in discrete and continuous time, Poisson processes, renewal processes, martingales, and option pricing. One can only learn a subject by seeing it in action, so there are a large number of examples and more than 300 carefully chosen exercises to deepen the reader’s understanding.
Drawing from teaching experience and student feedback, there are many new examples and problems with solutions that use TI-83 to eliminate the tedious details of solving linear equations by hand, and the collection of exercises is much improved, with many more biological examples. Originally included in previous editions, material too advanced for this first course in stochastic processes has been eliminated while treatment of other topics useful for applications has been expanded. In addition, the ordering of topics has been improved; for example, the difficult subject of martingales is delayed until its usefulness can be applied in the treatment of mathematical finance.
• A concise treatment and textbook on the most important topics in Stochastic Processes
• Illustrates all concepts with examples and presents more than 300 carefully chosen exercises for effective learning
• New edition includes added and revised exercises, including many biological exercises, in addition to restructured and rewritten sections with a goal toward clarity and simplicity
• Solutions Manual available for instructors

Rigorous probabilistic arguments, built on the foundation of measure theory introduced eighty years ago by Kolmogorov, have invaded many fields. Students of statistics, biostatistics, econometrics, finance, and other changing disciplines now find themselves needing to absorb theory beyond what they might have learned in the typical undergraduate, calculus-based probability course. This 2002 book grew from a one-semester course offered for many years to a mixed audience of graduate and undergraduate students who have not had the luxury of taking a course in measure theory. The core of the book covers the basic topics of independence, conditioning, martingales, convergence in distribution, and Fourier transforms. In addition there are numerous sections treating topics traditionally thought of as more advanced, such as coupling and the KMT strong approximation, option pricing via the equivalent martingale measure, and the isoperimetric inequality for Gaussian processes. The book is not just a presentation of mathematical theory, but is also a discussion of why that theory takes its current form. It will be a secure starting point for anyone who needs to invoke rigorous probabilistic arguments and understand what they mean.

This book explores weak convergence theory and empirical processes and their applications to many applications in statistics. Part one reviews stochastic convergence in its various forms. Part two offers the theory of empirical processes in a form accessible to statisticians and probabilists. Part three covers a range of topics demonstrating the applicability of the theory to key questions such as measures of goodness of fit and the bootstrap.

Concentration inequalities for functions of independent random variables is an area of probability theory that has witnessed a great revolution in the last few decades, and has applications in a wide variety of areas such as machine learning, statistics, discrete mathematics, and high-dimensional geometry. Roughly speaking, if a function of many independent random variables does not depend too much on any of the variables then it is concentrated in the sense that with high probability, it is close to its expected value. This book offers a host of inequalities to illustrate this rich theory in an accessible way by covering the key developments and applications in the field. The authors describe the interplay between the probabilistic structure (independence) and a variety of tools ranging from functional inequalities to transportation arguments to information theory. Applications to the study of empirical processes, random projections, random matrix theory, and threshold phenomena are also presented. A self-contained introduction to concentration inequalities, it includes a survey of concentration of sums of independent random variables, variance bounds, the entropy method, and the transportation method. Deep connections with isoperimetric problems are revealed whilst special attention is paid to applications to the supremum of empirical processes. Written by leading experts in the field and containing extensive exercise sections this book will be an invaluable resource for researchers and graduate students in mathematics, theoretical computer science, and engineering.

This text is an introduction to the modern theory and applications of probability and stochastics. The style and coverage is geared towards the theory of stochastic processes, but with some attention to the applications. In many instances the gist of the problem is introduced in practical, everyday language and then is made precise in mathematical form. The first four chapters are on probability theory: measure and integration, probability spaces, conditional expectations, and the classical limit theorems. There follows chapters on martingales, Poisson random measures, Levy Processes, Brownian motion, and Markov Processes. Special attention is paid to Poisson random measures and their roles in regulating the excursions of Brownian motion and the jumps of Levy and Markov processes. Each chapter has a large number of varied examples and exercises. The book is based on the author's lecture notes in courses offered over the years at Princeton University. These courses attracted graduate students from engineering, economics, physics, computer sciences, and mathematics. Erhan Cinlar has received many awards for excellence in teaching, including the President's Award for Distinguished Teaching at Princeton University. His research interests include theories of Markov processes, point processes, stochastic calculus, and stochastic flows. The book is full of insights and observations that only a lifetime researcher in probability can have, all told in a lucid yet precise style.