Introduction to Probability

Introduction to Probability An intuitive yet precise introduction to probability theory stochastic processes and probabilistic models used in science engineering economics and related fields The nd edition is a substantia

  • Title: Introduction to Probability
  • Author: Dimitri P. Bertsekas John N. Tsitsiklis
  • ISBN: 9781886529236
  • Page: 214
  • Format: Hardcover
  • An intuitive, yet precise introduction to probability theory, stochastic processes, and probabilistic models used in science, engineering, economics, and related fields The 2nd edition is a substantial revision of the 1st edition, involving a reorganization of old material and the addition of new material The length of the book has increased by about 25 percent The mainAn intuitive, yet precise introduction to probability theory, stochastic processes, and probabilistic models used in science, engineering, economics, and related fields The 2nd edition is a substantial revision of the 1st edition, involving a reorganization of old material and the addition of new material The length of the book has increased by about 25 percent The main new feature of the 2nd edition is thorough introduction to Bayesian and classical statistics The book is the currently used textbook for Probabilistic Systems Analysis, an introductory probability course at the Massachusetts Institute of Technology, attended by a large number of undergraduate and graduate students The book covers the fundamentals of probability theory probabilistic models, discrete and continuous random variables, multiple random variables, and limit theorems , which are typically part of a first course on the subject, as well as the fundamental concepts and methods of statistical inference, both Bayesian and classical It also contains, a number of advanced topics, from which an instructor can choose to match the goals of a particular course These topics include transforms, sums of random variables, a fairly detailed introduction to Bernoulli, Poisson, and Markov processes The book strikes a balance between simplicity in exposition and sophistication in analytical reasoning Some of the mathematically rigorous analysis has been just intuitively explained in the text, but is developed in detail at the level of advanced calculus in the numerous solved theoretical problems Written by two professors of the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, and members of the prestigious US National Academy of Engineering, the book has been widely adopted for classroom use in introductory probability courses within the USA and abroad.From a Review of the 1st Edition trains the intuition to acquire probabilistic feeling This book explains every single concept it enunciates This is its main strength, deep explanation, and not just examples that happen to explain Bertsekas and Tsitsiklis leave nothing to chance The probability to misinterpret a concept or not understand it is just zero Numerous examples, figures, and end of chapter problems strengthen the understanding Also of invaluable help is the book s web site, where solutions to the problems can be found as well as much information pertaining to probability, and also problem sets Vladimir Botchev, Analog Dialogue Several other reviews can be found in the listing of the first edition of this book Contents, preface, and info at publisher s website Athena Scientific, athenasc com

    Introduction to Probability, nd Edition An intuitive, yet precise introduction to probability theory, stochastic processes, and probabilistic models used in science, engineering, economics, and related fields The nd edition is a substantial revision of the st edition, involving a reorganization of old material and the addition of new material. Introduction to Probability Dartmouth College probability is covered, students should have taken as a prerequisite two terms of calculus, including an introduction to multiple integrals In order to cover Chap ter , which contains material on Markov chains, some knowledge of matrix theory is necessary The text can also be used in a discrete probability course The material has been Introduction to Probability Introduction to Probability Dimitri P Bertsekas and John N Tsitsiklis Professors of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge, Massachusetts These notes are copyright protected but may be freely distributed for instructional nonpro t pruposes. Introduction to Probability edx Probability and statistics help to bring logic to a world replete with randomness and uncertainty This course will give you tools needed to understand data, science, philosophy, engineering, economics, and finance You will learn not only how to solve challenging technical problems, but also how Introduction to Probability MIT OpenCourseWare The tools of probability theory, and of the related field of statistical inference, are the keys for being able to analyze and make sense of data These tools underlie important advances in many fields, from the basic sciences to engineering and management This resource is a companion site to Probabilistic Systems Analysis and Applied Probability. Introduction to Probability and Data Coursera Introduction to Probability and Data from Duke University This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes rule You will examine various types of sampling methods, and discuss how such Textbook Introduction to Probability, nd Edition An intuitive, yet precise introduction to probability theory, stochastic processes, statistical inference, and probabilistic models used in science, engineering, economics, and related fields This is the currently used textbook for Probabilistic Systems Analysis, an introductory probability course at the Massachusetts Institute of Technology

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    One thought on “Introduction to Probability”

    1. Well done textbook introducing all the main topics in probability, as well as Markov Chains, Bayesian Statistical Inference, and Classical Statistical Inference. I would also recommend the free MIT course at edX, Introduction to Probability - The Science of Uncertainty, taught by the author of this book, John Tsitsiklis.

    2. This is an excelent introduction to calculus based probability. It is easily accessible to people coming from any dicipline.I read it as part of my graduate studies at GMU (ECE 528).

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