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6-11-2015, 22:11

Markov Processes: Characterization and Convergence

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Markov Processes: Characterization and Convergence

Markov Processes: Characterization and Convergence
by Stewart N. Ethier, Thomas G. Kurtz
English | 1986 | ISBN: 0471081868 | 544 pages | PDF | 3.12 MB

“Recursive Estimation and Control for Stochastic Systems Han-Fu Chen This self-contained volume presents both the discrete-time and continuous-time systems, and incorporates not only well-known results in these fields but also many of the latest research findings. It shows how to analyze the convergence of recursive estimates through a combination of the probabilistic and ordinary differential equation methods and establishes the connection between the Gauss-Markov estimate and the Kalman filter through stochastic observability, and more. 1985 (0 471-81566-7) 378 pp. Nonparametric Density Estimation The L 1 View Luc Devroye and Laszlo Gyorfi The first systematic, single-source examination that develops from first principles the "natural" theory for density estimation and shows why the classical L 2 theory masks some fundamental properties of density estimates. Linking different subareas of statistics, including simulation, pattern recognition, detection theory, and minimax theory, it shows how to construct, use, and analyze density estimates. Relevant recent literature is tied in with the classical works of Parzen, Rosenblatt, and others. 1985 (0 471-81646-9) 368 pp. Elements of Applied Stochastic Processes Second Edition U. Narayan Bhat An applied introduction to stochastic models, this expanded and revised account develops basic concepts and techniques and applies them to problems arising in queueing, reliability, inventory and computer communications, social and behavioral processes, business management, and time series analysis. 1984 (0 471-87826-X) 736 pp.”



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