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Rigorous exposition suitable for elementary instruction. Covers measure theory, axiomatization of probability theory, processes with independent increments, Markov processes and limit theorems for random processes, more. A wealth of results, ideas, and techniques distinguish this text. Introduction. Bibliography. 1969 edition.
The long-awaited revision of Fundamentals of Applied Probability and Random Processes expands on the central components that made the first edition a classic. The title is based on the premise that engineers use probability as a modeling tool, and that probability can be applied to the solution of engineering problems. Engineers and students studying probability and random processes also need to analyze data, and thus need some knowledge of statistics. This book is designed to provide students with a thorough grounding in probability and stochastic processes, demonstrate their applicability to real-world problems, and introduce the basics of statistics. The book's clear writing style and homework problems make it ideal for the classroom or for self-study. Demonstrates concepts with more than 100 illustrations, including 2 dozen new drawings Expands readers’ understanding of disruptive statistics in a new chapter (chapter 8) Provides new chapter on Introduction to Random Processes with 14 new illustrations and tables explaining key concepts. Includes two chapters devoted to the two branches of statistics, namely descriptive statistics (chapter 8) and inferential (or inductive) statistics (chapter 9).
The book covers basic concepts such as random experiments, probability axioms, conditional probability, and counting methods, single and multiple random variables (discrete, continuous, and mixed), as well as moment-generating functions, characteristic functions, random vectors, and inequalities; limit theorems and convergence; introduction to Bayesian and classical statistics; random processes including processing of random signals, Poisson processes, discrete-time and continuous-time Markov chains, and Brownian motion; simulation using MATLAB and R.
This engaging introduction to random processes provides students with the critical tools needed to design and evaluate engineering systems that must operate reliably in uncertain environments. A brief review of probability theory and real analysis of deterministic functions sets the stage for understanding random processes, whilst the underlying measure theoretic notions are explained in an intuitive, straightforward style. Students will learn to manage the complexity of randomness through the use of simple classes of random processes, statistical means and correlations, asymptotic analysis, sampling, and effective algorithms. Key topics covered include: • Calculus of random processes in linear systems • Kalman and Wiener filtering • Hidden Markov models for statistical inference • The estimation maximization (EM) algorithm • An introduction to martingales and concentration inequalities. Understanding of the key concepts is reinforced through over 100 worked examples and 300 thoroughly tested homework problems (half of which are solved in detail at the end of the book).
Clear presentation employs methods that recognize computer-related aspects of theory. Topics include expectations and independence, Bernoulli processes and sums of independent random variables, Markov chains, renewal theory, more. 1975 edition.
For most people, intuitive notions concerning probabilities are connected with relative frequencies of occurrence. For example, when we say that in toss­ ing a coin, the probability of its coming up "heads" is 1/2, we usually mean that in a large number of tosses, about 1/2 of the tosses will come up heads. Unfortunately, relative frequency of occurrence has proved to be an unsatis­ factory starting point in defining probability. Although there have been attempts to make frequency of occurrence part of the axiomatic structure of probability theory, the currently accepted formu1ation is one based on measure theory due to Ko1mogorov. In this formulation frequency of occurrence is an interpretation for probability rather than adefinition. This inter­ pretation is justified under suitab1e conditions by the 1aw of 1arge numbers. The starting point of probability theory is usua11y taken to be an experi­ ment the outcome of which is not fixed apriori. Some fami1iar examples inc1ude tossing a die, observation of a noise vo1tage at a fixed time, the error in measuring a physica1 parameter, and the exact touchdown time of an aircraft. Let ~ denote the set of all possib1e outcomes of an experiment. For examp1e, for the experiment of tossing one die, ~ = {1, 2, 3, 4, 5, 6}, whi1e for the touchdown time of an aircraft, ~ might be chosen to be 0 ~ t
Detailed coverage of probability theory, random variables and their functions, stochastic processes, linear system response to stochastic processes, Gaussian and Markov processes, and stochastic differential equations. 1973 edition.