Probabilistic Models For Dynamical Systems Second Edition
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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.
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 explo
Handbook Of Dynamics And Probability
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Author : Peter Müller
language : en
Publisher: Springer Nature
Release Date : 2021-11-20
Handbook Of Dynamics And Probability written by Peter Müller and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-20 with Science categories.
Our time is characterized by an explosive growth in the use of ever more complicated and sophisticated (computer) models. These models rely on dynamical systems theory for the interpretation of their results and on probability theory for the quantification of their uncertainties. A conscientious and intelligent use of these models requires that both these theories are properly understood. This book is to provide such understanding. It gives a unifying treatment of dynamical systems theory and probability theory. It covers the basic concepts and statements of these theories, their interrelations, and their applications to scientific reasoning and physics. The book stresses the underlying concepts and mathematical structures but is written in a simple and illuminating manner without sacrificing too much mathematical rigor. The book is aimed at students, post-docs, and researchers in the applied sciences who aspire to better understand the conceptual and mathematical underpinnings of the models that they use. Despite the peculiarities of any applied science, dynamics and probability are the common and indispensable tools in any modeling effort. The book is self-contained, with many technical aspects covered in appendices, but does require some basic knowledge in analysis, linear algebra, and physics. Peter Müller, now a professor emeritus at the University of Hawaii, has worked extensively on ocean and climate models and the foundations of complex system theories.
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.
Hidden Markov Models And Dynamical Systems
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Author : Andrew M. Fraser
language : en
Publisher: SIAM
Release Date : 2008-01-01
Hidden Markov Models And Dynamical Systems written by Andrew M. Fraser and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-01-01 with Mathematics categories.
This text provides an introduction to hidden Markov models (HMMs) for the dynamical systems community. It is a valuable text for third or fourth year undergraduates studying engineering, mathematics, or science that includes work in probability, linear algebra and differential equations. The book presents algorithms for using HMMs, and it explains the derivation of those algorithms. It presents Kalman filtering as the extension to a continuous state space of a basic HMM algorithm. The book concludes with an application to biomedical signals. This text is distinctive for providing essential introductory material as well as presenting enough of the theory behind the basic algorithms so that the reader can use it as a guide to developing their own variants.
Mathematics For The Life Sciences
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Author : Glenn Ledder
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-08-29
Mathematics For The Life Sciences written by Glenn Ledder 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 2013-08-29 with Mathematics categories.
Mathematics for the Life Sciences provides present and future biologists with the mathematical concepts and tools needed to understand and use mathematical models and read advanced mathematical biology books. It presents mathematics in biological contexts, focusing on the central mathematical ideas, and providing detailed explanations. The author assumes no mathematics background beyond algebra and precalculus. Calculus is presented as a one-chapter primer that is suitable for readers who have not studied the subject before, as well as readers who have taken a calculus course and need a review. This primer is followed by a novel chapter on mathematical modeling that begins with discussions of biological data and the basic principles of modeling. The remainder of the chapter introduces the reader to topics in mechanistic modeling (deriving models from biological assumptions) and empirical modeling (using data to parameterize and select models). The modeling chapter contains a thorough treatment of key ideas and techniques that are often neglected in mathematics books. It also provides the reader with a sophisticated viewpoint and the essential background needed to make full use of the remainder of the book, which includes two chapters on probability and its applications to inferential statistics and three chapters on discrete and continuous dynamical systems. The biological content of the book is self-contained and includes many basic biology topics such as the genetic code, Mendelian genetics, population dynamics, predator-prey relationships, epidemiology, and immunology. The large number of problem sets include some drill problems along with a large number of case studies. The latter are divided into step-by-step problems and sorted into the appropriate section, allowing readers to gradually develop complete investigations from understanding the biological assumptions to a complete analysis.
Handbook Of Probabilistic Models
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Author : Pijush Samui
language : en
Publisher: Butterworth-Heinemann
Release Date : 2019-10-05
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 2019-10-05 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. Explains the application of advanced probabilistic models encompassing multidisciplinary research Applies probabilistic modeling to emerging areas in engineering Provides an interdisciplinary approach to probabilistic models and their applications, thus solving a wide range of practical problems
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.
Statistical Physics
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Author : Josef Honerkamp
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
Statistical Physics written by Josef Honerkamp 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 2013-03-09 with Science categories.
The book is divided into two parts. The first part looks at the modeling of statistical systems before moving on to an analysis of these systems. This second edition contains new material on: estimators based on a probability distribution for the parameters; identification of stochastic models from observations; and statistical tests and classification methods.
Modelling And Control Of Dynamic Systems Using Gaussian Process Models
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Author : Juš Kocijan
language : en
Publisher: Springer
Release Date : 2015-11-21
Modelling And Control Of Dynamic Systems Using Gaussian Process Models written by Juš Kocijan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-21 with Technology & Engineering categories.
This monograph opens up new horizons for engineers and researchers in academia and in industry dealing with or interested in new developments in the field of system identification and control. It emphasizes guidelines for working solutions and practical advice for their implementation rather than the theoretical background of Gaussian process (GP) models. The book demonstrates the potential of this recent development in probabilistic machine-learning methods and gives the reader an intuitive understanding of the topic. The current state of the art is treated along with possible future directions for research. Systems control design relies on mathematical models and these may be developed from measurement data. This process of system identification, when based on GP models, can play an integral part of control design in data-based control and its description as such is an essential aspect of the text. The background of GP regression is introduced first with system identification and incorporation of prior knowledge then leading into full-blown control. The book is illustrated by extensive use of examples, line drawings, and graphical presentation of computer-simulation results and plant measurements. The research results presented are applied in real-life case studies drawn from successful applications including: a gas–liquid separator control; urban-traffic signal modelling and reconstruction; and prediction of atmospheric ozone concentration. A MATLAB® toolbox, for identification and simulation of dynamic GP models is provided for download.
Linear Dynamical Systems
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Author : Mircea D. Grigoriu
language : en
Publisher: Springer Nature
Release Date : 2021-01-30
Linear Dynamical Systems written by Mircea D. Grigoriu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-30 with Science categories.
This textbook provides a concise, clear, and rigorous presentation of the dynamics of linear systems that delivers the necessary tools for the analysis and design of mechanical/ structural systems, regardless of their complexity. The book is written for senior undergraduate and first year graduate students as well as engineers working on the design of mechanical/structural systems subjected to dynamic actions, such as wind/earthquake engineers and mechanical engineers working on wind turbines. Professor Grigoriu's lucid presentation maximizes student understanding of the formulation and the solution of linear systems subjected to dynamic actions, and provides a clear distinction between problems of practical interest and their special cases. Based on the author's lecture notes from courses taught at Cornell University, the material is class-tested over many years and ideal as a core text for a range of classes in mechanical, civil, and geotechnical engineering, as well as for self-directed learning by practitioners in the field.