4 edition of Parameter estimation found in the catalog.
Includes bibliographies and index.
|Statement||Harold W. Sorenson.|
|Series||Control and systems theory ; v. 9|
|LC Classifications||QA402 .S698|
|The Physical Object|
|Pagination||xi, 382 p. :|
|Number of Pages||382|
|LC Control Number||80019075|
Parameter Estimation and Inverse Problems, Second Edition provides geoscience students and professionals with answers to common questions like how one can derive a physical model from a finite set of observations containing errors, and how one may determine the quality of such a model. This book takes on these fundamental and challenging problems, introducing students and professionals to the. Classification, 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.
This book focuses on the meaning of statistical inference and estimation. Statistical inference is concerned with the problems of estimation of population parameters and testing hypotheses. Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain. There are many techniques for solving density estimation, although a common framework used throughout the field of machine learning is maximum likelihood estimation. Maximum likelihood estimation involves defining a likelihood function for calculating the conditional.
Parameter 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. Parameter estimation is the art of adjusting the parameters of an analytical model of a structure to reproduce measured data (static or dynamic). Parameter estimation also can be used to identify element parameters implicit in the stiffness or mass matrices describing a structural system at the component level and is a useful tool for finite.
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The subject of this book is estimating parameters of expectation models of statistical observations. The book describes the most important aspects of the subject for applied scientists and engineers. This group of Parameter estimation book is often not aware of estimators other than least by: Parameter Estimation and Inverse Problems, Second Edition introduces readers to both Classical and Bayesian approaches to linear and nonlinear problems with particular attention paid to computational, mathematical, and statistical issues related to their application to geophysical problems.
The textbook includes Appendices covering essential linear algebra, statistics, and notation in the context of the /5(7).
Parameter Estimation and Inverse Problems primarily serves as a Parameter estimation book for advanced undergraduate and introductory graduate courses. It promotes a fundamental understanding of parameter estimation and inverse problem philosophy and methodology/5(4). In the book a unified and in-depth physical and mathematical analysis of the various parameter estimators and condition monitoring methods is presented.
For this purpose, where possible, space phasor theory is utilized and the most recent and modern developments in the field are incorporated/5. Great book, methodical, extremely well written.
out of 5 stars Parameter Estimation, Condition Monitoring, and Diagnosis of Reviewed in the United States on Febru If you need an engineering introduction into parameter estimation for generators, motors, transformers, and even more this book is the one you should be looking by: The subject of this book is estimating parameters of expectation models of statistical observations.
The book describes the most important aspects of the subject for applied scientists and engineers. This group of users is often not aware of estimators other than least squares. The book is a thorough review of parameter estimation in most important item response theory models. For me, the outstanding part is the one that clearly distinguish the Rasch family from the /5(6).
Parameter Estimation and Inverse Problems, Second Edition introduces readers to both Classical and Bayesian approaches to linear and nonlinear problems with particular attention paid to computational, mathematical, and statistical issues related to their application to geophysical problems.
The textbook includes Appendices covering essential linear algebra, statistics, and notation in the context of the. International Society of Parametric Analysts Parametric Estimating Handbook© Fourth Edition – April Generate Reference Book: File may be more up-to-date The term parameter estimation refers to the process of using sample data (in reliability engineering, usually times-to-failure or success data) to estimate the parameters of the selected distribution.
Several parameter estimation. Parameter estimation of interest rate models When using the interest rate models for pricing or simulation purposes, it is important to calibrate their parameters to real data properly.
Here, we present a possible method to estimate the parameters. This method was developed by Chan et al,and is often referred to as the CKLS method. Item Response Theory clearly describes the most recently developed IRT models and furnishes detailed explanations of algorithms that can be used to estimate the item or ability parameters under various IRT models.
Extensively revised and expanded, this edition offers three new chapters discussing parameter estimation with multiple groups, parameter estimation for a test with mixed.
Book Abstract: Bayesian Bounds provides a collection of the important papers dealing with the theory and application of Bayesian bounds.
The book is essential to both engineers and statisticians whether they are practitioners or theorists. Each part of the book is introduced with the contributions of each selected paper and their interrelationship. Parameter estimation in stochastic differential equations and stochastic partial differential equations is the science, art and technology of modelling complex phenomena and making beautiful decisions.
The subject has attracted researchers from several areas of mathematics and other related fields like economics and finance. Introduction 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 5/5(1).
Parameter Estimation and Inverse Problems primarily serves as a textbook for advanced undergraduate and introductory graduate courses. Class notes have been developed and reside on the World Wide 5/5(1).
•Parameter Estimation (this lecture) •Non-parametric Density Estimation (the next two lectures) • Parameter estimation –Assume a particular form for the density (e.g.
Gaussian), so only the parameters (e.g., mean and variance) need to be estimated •Maximum Likelihood •Bayesian Estimation • Non-parametric density estimationFile Size: KB. Parameter estimation is a key step in system identification.
In the third chapter, we give a brief but comprehensive overview of information theoretic approaches for parameter estimation, such as the maximum entropy estimation, minimum divergence estimation, and minimum error entropy estimation, and discuss the connections between information theoretic methods and conventional alternatives.
Parameter Estimation - H. Sorenson Covers same ground as textbook but in a different order; thus, provides an interesting alternative view. Has appendices on Matrices and Probability Theory - a little more detailed than textbook.
Chapter 4 Parameter Estimation Thus far we have concerned ourselves primarily with probability theory: what events may occur with what probabilities, given a model family and choices for the parameters. This is useful only in the case where we know the precise model family and parameter values for the situation of Size: KB.
2 Parameter Estimation LDA is a generative probabilistic model, so to understand exactly how this works we need to understand the underlying probability distributions. In this chapter we will focus on the Bernoulli distribution and the Beta distribution.This book discusses the field of parameter estimation where parameters within a mathematical model are estimated through indirect measurements.
As with the inverse heat conduction problem, the estimation of the parameters is hindered due to the presence of measurement errors.The basic ideas behind the parameter estimation methods are discussed in a general setting.
The application to estimation or parameters in dynamical systems is treated in detail using the prototype problem of estimating parameters in a continuous time system using discrete time measurements.
Computational aspects are discussed.