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Probabilistic Models For Dynamical Systems

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Probabilistic Models For Dynamical Systems

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Author : Haym Benaroya
language : en
Publisher: CRC Press
Release Date : 2013-05-02

Probabilistic Models For Dynamical Systems written by Haym Benaroya and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-05-02 with Mathematics categories.

Probabilistic Models For Dynamical Systems 2nd Edition

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Author : Haym Benaroya
language : en
Publisher:
Release Date : 2013

Probabilistic Models For Dynamical Systems 2nd Edition written by Haym Benaroya and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.

Now in its second edition, Probabilistic Models for Dynamical Systems expands on the subject of probability theory. Written as an extension to its predecessor, this revised version introduces students to the randomness in variables and time dependent functions, and allows them to solve governing equations. Introduces probabilistic modeling and explores applications in a wide range of engineering fields Identifies and draws on specialized texts and papers published in the literature Develops the theoretical underpinnings and covers approximation methods and numerical methods Presents material relevant to students in various engineering disciplines as well as professionals in the field This book provides a suitable resource for self-study and can be used as an all-inclusive introduction to probability for engineering. It presents basic concepts, presents history and insight, and highlights applied probability in a practical manner. With updated information, this edition includes new sections, problems, applications, and examples. Biographical summaries spotlight relevant historical figures, providing life sketches, their contributions, relevant quotes, and what makes them noteworthy. A new chapter on control and mechatronics, and over 300 illustrations rounds out the coverage.

Dynamic Probabilistic Systems Volume I

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Author : Ronald A. Howard
language : en
Publisher: Courier Corporation
Release Date : 2007-06-05

Dynamic Probabilistic Systems Volume I written by Ronald A. Howard and has been published by Courier Corporation this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-06-05 with Mathematics categories.

An integrated work in two volumes, this text teaches readers to formulate, analyze, and evaluate Markov models. The first volume treats basic process; the second, semi-Markov and decision processes. 1971 edition.

Handbook Of Probabilistic Models

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Author : Pijush Samui
language : en
Publisher: Butterworth-Heinemann
Release Date : 2019-10-08

Handbook Of Probabilistic Models written by Pijush Samui and has been published by Butterworth-Heinemann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-08 with Computers categories.

Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook, practitioners, researchers and scientists will find detailed explanations of technical concepts, applications of the proposed methods, and the respective scientific approaches needed to solve the problem. This book provides an interdisciplinary approach that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil engineering, to electronics, electrical, earth sciences, climate, agriculture, water resource, mathematical sciences and computer sciences. Specific topics covered include minimax probability machine regression, stochastic finite element method, relevance vector machine, logistic regression, Monte Carlo simulations, random matrix, Gaussian process regression, Kalman filter, stochastic optimization, maximum likelihood, Bayesian inference, Bayesian update, kriging, copula-statistical models, and more.

Probabilistic Boolean Networks

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Author : Ilya Shmulevich
language : en
Publisher: SIAM
Release Date : 2010-01-01

Probabilistic Boolean Networks written by Ilya Shmulevich and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-01-01 with Mathematics categories.

This is the first comprehensive treatment of probabilistic Boolean networks (PBNs), an important model class for studying genetic regulatory networks. This book covers basic model properties, including the relationships between network structure and dynamics, steady-state analysis, and relationships to other model classes." "Researchers in mathematics, computer science, and engineering are exposed to important applications in systems biology and presented with ample opportunities for developing new approaches and methods. The book is also appropriate for advanced undergraduates, graduate students, and scientists working in the fields of computational biology, genomic signal processing, control and systems theory, and computer science.

Probability And Partial Differential Equations In Modern Applied Mathematics

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Author : Edward C. Waymire
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-06-14

Probability And Partial Differential Equations In Modern Applied Mathematics written by Edward C. Waymire and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-06-14 with Mathematics categories.

"Probability and Partial Differential Equations in Modern Applied Mathematics" is devoted to the role of probabilistic methods in modern applied mathematics from the perspectives of both a tool for analysis and as a tool in modeling. There is a recognition in the applied mathematics research community that stochastic methods are playing an increasingly prominent role in the formulation and analysis of diverse problems of contemporary interest in the sciences and engineering. A probabilistic representation of solutions to partial differential equations that arise as deterministic models allows one to exploit the power of stochastic calculus and probabilistic limit theory in the analysis of deterministic problems, as well as to offer new perspectives on the phenomena for modeling purposes. There is also a growing appreciation of the role for the inclusion of stochastic effects in the modeling of complex systems. This has led to interesting new mathematical problems at the interface of probability, dynamical systems, numerical analysis, and partial differential equations. This volume will be useful to researchers and graduate students interested in probabilistic methods, dynamical systems approaches and numerical analysis for mathematical modeling in the sciences and engineering.

Probabilistic Graphical Models

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Author : Daphne Koller
language : en
Publisher: MIT Press
Release Date : 2009-07-31

Probabilistic Graphical Models written by Daphne Koller and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-07-31 with Computers categories.

A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

Handbook Of Probabilistic Models

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Author : Pijush Samui
language : en
Publisher: Butterworth-Heinemann
Release Date : 2019-10-05

Dynamic Probabilistic Models And Social Structure

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Author : Guillermo L. Gómez M.
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Dynamic Probabilistic Models And Social Structure written by Guillermo L. Gómez M. and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.

Mathematical models have been very successful in the study of the physical world. Galilei and Newton introduced point particles moving without friction under the action of simple forces as the basis for the description of concrete motions like the ones of the planets. This approach was sustained by appro priate mathematical methods, namely infinitesimal calculus, which was being developed at that time. In this way classical analytical mechanics was able to establish some general results, gaining insight through explicit solution of some simple cases and developing various methods of approximation for handling more complicated ones. Special relativity theory can be seen as an extension of this kind of modelling. In the study of electromagnetic phenomena and in general relativity another mathematical model is used, in which the concept of classical field plays the fundamental role. The equations of motion here are partial differential equations, and the methods of study used involve further developments of classical analysis. These models are deterministic in nature. However it was realized already in the second half of last century, through the work of Maxwell, Boltzmann, Gibbs and others, that in the discussion of systems involving a great number of particles, the deterministic description is not by itself of great help, in particu lar a suitable "weighting" of all possible initial conditions should be considered.

Modeling Of Dynamic Systems With Engineering Applications

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Author : Clarence W. de Silva
language : en
Publisher: CRC Press
Release Date : 2017-10-16

Modeling Of Dynamic Systems With Engineering Applications written by Clarence W. de Silva and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-16 with Technology & Engineering categories.

MODELING OF DYNAMIC SYSTEMS takes a unique, up-to-date approach to systems dynamics and related controls coverage for undergraduate students and practicing engineers. It focuses on the model development of engineering problems rather than response analysis and simulation once a model is available, though these are also covered. Linear graphing and bond graph approaches are both discussed, and computational tools are integrated thoughout. Electrical, mechanical, fluid, and thermal domains are covered, as are problems of multiple domains (mixed systems); the unified and integrated approaches taken are rapidly becoming the standard in the modeling of mechatronic engineering systems.

Probabilistic Models For Dynamical Systems

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