Parameter Estimation for Scientists and Engineers
Author: Adriaan van den Bos
Publisher: Wiley-Interscience
Published: 2007-07-16
Total Pages: 296
ISBN-13:
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Parameter Estimation and Inverse Problems
Author: Richard C. Aster
Publisher: Elsevier
Published: 2018-10-16
Total Pages: 406
ISBN-13: 0128134232
DOWNLOAD EBOOKParameter Estimation and Inverse Problems, Third Edition, is structured around a course at New Mexico Tech and is designed to be accessible to typical graduate students in the physical sciences who do not have an extensive mathematical background. The book is complemented by a companion website that includes MATLAB codes that correspond to examples that are illustrated with simple, easy to follow problems that illuminate the details of particular numerical methods. Updates to the new edition include more discussions of Laplacian smoothing, an expansion of basis function exercises, the addition of stochastic descent, an improved presentation of Fourier methods and exercises, and more. - Features examples that are illustrated with simple, easy to follow problems that illuminate the details of a particular numerical method - Includes an online instructor's guide that helps professors teach and customize exercises and select homework problems - Covers updated information on adjoint methods that are presented in an accessible manner
Parameter Estimation and Hypothesis Testing in Linear Models
Author: Karl-Rudolf Koch
Publisher: Springer Science & Business Media
Published: 2013-03-09
Total Pages: 344
ISBN-13: 3662039761
DOWNLOAD EBOOKA treatment of estimating unknown parameters, testing hypotheses and estimating confidence intervals in linear models. Readers will find here presentations of the Gauss-Markoff model, the analysis of variance, the multivariate model, the model with unknown variance and covariance components and the regression model as well as the mixed model for estimating random parameters. A chapter on the robust estimation of parameters and several examples have been added to this second edition. The necessary theorems of vector and matrix algebra and the probability distributions of test statistics are derived so as to make this book self-contained. Geodesy students as well as those in the natural sciences and engineering will find the emphasis on the geodetic application of statistical models extremely useful.
Parameter Estimation in Engineering and Science
Author: James Vere Beck
Publisher: James Beck
Published: 1977
Total Pages: 540
ISBN-13: 9780471061182
DOWNLOAD EBOOKIntroduction to and survey of parameter estimation; Probability; Introduction to statistics; Parameter estimation methods; Introduction to linear estimation; Matrix analysis for linear parameter estimation; Minimization of sum of squares functions for models nonlinear in parameters; Design of optimal experiments.
Parameter Estimation in Stochastic Differential Equations
Author: Jaya P. N. Bishwal
Publisher: Springer
Published: 2007-09-26
Total Pages: 271
ISBN-13: 3540744487
DOWNLOAD EBOOKParameter estimation in stochastic differential equations and stochastic partial differential equations is the science, art and technology of modeling complex phenomena. The subject has attracted researchers from several areas of mathematics. This volume presents the estimation of the unknown parameters in the corresponding continuous models based on continuous and discrete observations and examines extensively maximum likelihood, minimum contrast and Bayesian methods.
Modelling and Parameter Estimation of Dynamic Systems
Author: J.R. Raol
Publisher: IET
Published: 2004-08-13
Total Pages: 405
ISBN-13: 0863413633
DOWNLOAD EBOOKThis book presents a detailed examination of the estimation techniques and modeling problems. The theory is furnished with several illustrations and computer programs to promote better understanding of system modeling and parameter estimation.
Classification, Parameter Estimation and State Estimation
Author: Ferdinand van der Heijden
Publisher: John Wiley & Sons
Published: 2005-06-10
Total Pages: 440
ISBN-13: 0470090146
DOWNLOAD EBOOKClassification, Parameter Estimation and State Estimation is a practical guide for data analysts and designers of measurement systems and postgraduates students that are interested in advanced measurement systems using MATLAB. 'Prtools' is a powerful MATLAB toolbox for pattern recognition and is written and owned by one of the co-authors, B. Duin of the Delft University of Technology. After an introductory chapter, the book provides the theoretical construction for classification, estimation and state estimation. The book also deals with the skills required to bring the theoretical concepts to practical systems, and how to evaluate these systems. Together with the many examples in the chapters, the book is accompanied by a MATLAB toolbox for pattern recognition and classification. The appendix provides the necessary documentation for this toolbox as well as an overview of the most useful functions from these toolboxes. With its integrated and unified approach to classification, parameter estimation and state estimation, this book is a suitable practical supplement in existing university courses in pattern classification, optimal estimation and data analysis. Covers all contemporary main methods for classification and estimation. Integrated approach to classification, parameter estimation and state estimation Highlights the practical deployment of theoretical issues. Provides a concise and practical approach supported by MATLAB toolbox. Offers exercises at the end of each chapter and numerous worked out examples. PRtools toolbox (MATLAB) and code of worked out examples available from the internet Many examples showing implementations in MATLAB Enables students to practice their skills using a MATLAB environment
Inverse Problem Theory and Methods for Model Parameter Estimation
Author: Albert Tarantola
Publisher: SIAM
Published: 2005-01-01
Total Pages: 349
ISBN-13: 9780898717921
DOWNLOAD EBOOKWhile the prediction of observations is a forward problem, the use of actual observations to infer the properties of a model is an inverse problem. Inverse problems are difficult because they may not have a unique solution. The description of uncertainties plays a central role in the theory, which is based on probability theory. This book proposes a general approach that is valid for linear as well as for nonlinear problems. The philosophy is essentially probabilistic and allows the reader to understand the basic difficulties appearing in the resolution of inverse problems. The book attempts to explain how a method of acquisition of information can be applied to actual real-world problems, and many of the arguments are heuristic.
Applied Parameter Estimation for Chemical Engineers
Author: Peter Englezos
Publisher: CRC Press
Published: 2000-10-12
Total Pages: 462
ISBN-13: 9780203904695
DOWNLOAD EBOOKThis book determines adjustable parameters in mathematical models that describe steady state or dynamic systems, presenting the most important optimization methods used for parameter estimation. It focuses on the Gauss-Newton method and its modifications for systems and processes represented by algebraic or differential equation models.
Bayesian Spectrum Analysis and Parameter Estimation
Author: G. Larry Bretthorst
Publisher: Springer Science & Business Media
Published: 2013-03-09
Total Pages: 210
ISBN-13: 146849399X
DOWNLOAD EBOOKThis work is essentially an extensive revision of my Ph.D. dissertation, [1J. It 1S primarily a research document on the application of probability theory to the parameter estimation problem. The people who will be interested in this material are physicists, economists, and engineers who have to deal with data on a daily basis; consequently, we have included a great deal of introductory and tutorial material. Any person with the equivalent of the mathematics background required for the graduate level study of physics should be able to follow the material contained in this book, though not without eIfort. From the time the dissertation was written until now (approximately one year) our understanding of the parameter estimation problem has changed extensively. We have tried to incorporate what we have learned into this book. I am indebted to a number of people who have aided me in preparing this docu ment: Dr. C. Ray Smith, Steve Finney, Juana Sunchez, Matthew Self, and Dr. Pat Gibbons who acted as readers and editors. In addition, I must extend my deepest thanks to Dr. Joseph Ackerman for his support during the time this manuscript was being prepared.
