Probabilistic Systems Analysis for the Estimation of Variances and Covariances PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Probabilistic Systems Analysis for the Estimation of Variances and Covariances PDF full book. Access full book title Probabilistic Systems Analysis for the Estimation of Variances and Covariances by El hadi M. Shakshuki. Download full books in PDF and EPUB format.
Author: El hadi M. Shakshuki
Publisher:
ISBN:
Category :
Languages : en
Pages : 230
Get Book Here
Book Description
Author: El hadi M. Shakshuki
Publisher:
ISBN:
Category :
Languages : en
Pages : 230
Get Book Here
Book Description
Author: Arthur M. Breipohl
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 376
Get Book Here
Book Description
Elementary probability; Engineering applications of probability; Random variables; Expected values; Distribution of functions of Random variables; Applications of Random variables to systems problems; Distributions from data; Estimation; Engineering decisions; Introduction to Random processes; Systems and Random signals.
Author: Ennio Cascetta
Publisher: Springer Science & Business Media
ISBN: 0387758577
Category : Business & Economics
Languages : en
Pages : 753
Get Book Here
Book Description
"This book provides a rigorous and comprehensive coverage of transportation models and planning methods and is a must-have to anyone in the transportation community, including students, teachers, and practitioners." Moshe Ben-Akiva, Massachusetts Institute of Technology.
Author: Paul D. Abramson
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 354
Get Book Here
Book Description
An optimal procedure for estimating the state of a linear dynamical system when the statistics of the measurement and process noise are poorly known is developed. The criterion of maximum likelihood is used to obtain an optimal estimate of the state and noise statistics. These estimates are shown to be asymptotically unbiased, efficient, and unique, with the estimation error normally distributed with a known covariance. The resulting equations for the estimates cannot be solved recursively, but an iterative procedure for their solution is presented. Several approximate solutions are presented which reduce the necessary computations in finding the estimates. Some of the approximate solutions allow a real time estimation of the state and noise statistics. Closely related to the estimation problem is the subject of hypothesis testing. Several criteria are developed for testing hypotheses concerning the values of the noise statistics that are used in the computation of the appropriate filter gains in a linear Kalman type state estimator. If the observed measurements are not consistent with the assumptions about the noise statistics, then estimation of the noise statistics should be undertaken using either optimal or suboptimal procedures. Numerical results of a digital computer simulation of the optimal and suboptimal solutions of the estimation problem are presented for a simple but realistic example.
Author: P. R. Kumar
Publisher: SIAM
ISBN: 1611974267
Category : Mathematics
Languages : en
Pages : 371
Get Book Here
Book Description
Since its origins in the 1940s, the subject of decision making under uncertainty has grown into a diversified area with application in several branches of engineering and in those areas of the social sciences concerned with policy analysis and prescription. These approaches required a computing capacity too expensive for the time, until the ability to collect and process huge quantities of data engendered an explosion of work in the area. This book provides succinct and rigorous treatment of the foundations of stochastic control; a unified approach to filtering, estimation, prediction, and stochastic and adaptive control; and the conceptual framework necessary to understand current trends in stochastic control, data mining, machine learning, and robotics.?
Author: Terrence P. McGarty
Publisher: Wiley-Interscience
ISBN:
Category : Mathematics
Languages : en
Pages : 426
Get Book Here
Book Description
Author: Ronald Christensen
Publisher: CRC Press
ISBN: 9780412062919
Category : Mathematics
Languages : en
Pages : 608
Get Book Here
Book Description
This text presents a comprehensive treatment of basic statistical methods and their applications. It focuses on the analysis of variance and regression, but also addressing basic ideas in experimental design and count data. The book has four connecting themes: similarity of inferential procedures, balanced one-way analysis of variance, comparison of models, and checking assumptions. Most inferential procedures are based on identifying a scalar parameter of interest, estimating that parameter, obtaining the standard error of the estimate, and identifying the appropriate reference distribution. Given these items, the inferential procedures are identical for various parameters. Balanced one-way analysis of variance has a simple, intuitive interpretation in terms of comparing the sample variance of the group means with the mean of the sample variance for each group. All balanced analysis of variance problems are considered in terms of computing sample variances for various group means. Comparing different models provides a structure for examining both balanced and unbalanced analysis of variance problems and regression problems. Checking assumptions is presented as a crucial part of every statistical analysis. Examples using real data from a wide variety of fields are used to motivate theory. Christensen consistently examines residual plots and presents alternative analyses using different transformation and case deletions. Detailed examination of interactions, three factor analysis of variance, and a split-plot design with four factors are included. The numerous exercises emphasize analysis of real data. Senior undergraduate and graduate students in statistics and graduate students in other disciplines using analysis of variance, design of experiments, or regression analysis will find this book useful.
Author: Werner Schiehlen
Publisher: Springer
ISBN: 370912820X
Category : Mathematics
Languages : en
Pages : 352
Get Book Here
Book Description
This book summarizes the developments in stochastic analysis and estimation. It presents novel applications to practical problems in mechanical systems. The main aspects of the course are random vibrations of discrete and continuous systems, analysis of nonlinear and parametric systems, stochastic modelling of fatigue damage, parameter estimation and identification with applications to vehicle road systems and process simulations by means of autoregressive models. The contributions will be of interest to engineers and research workers in industries and universities who want first hand information on present trends and problems in this topical field of engineering dynamics.
Author:
Publisher:
ISBN:
Category : Forest and forestry
Languages : en
Pages : 440
Get Book Here
Book Description
Author: The Analytic Sciences Corporation
Publisher: MIT Press
ISBN: 9780262570480
Category : Computers
Languages : en
Pages : 388
Get Book Here
Book Description
This is the first book on the optimal estimation that places its major emphasis on practical applications, treating the subject more from an engineering than a mathematical orientation. Even so, theoretical and mathematical concepts are introduced and developed sufficiently to make the book a self-contained source of instruction for readers without prior knowledge of the basic principles of the field. The work is the product of the technical staff of The Analytic Sciences Corporation (TASC), an organization whose success has resulted largely from its applications of optimal estimation techniques to a wide variety of real situations involving large-scale systems. Arthur Gelb writes in the Foreword that "It is our intent throughout to provide a simple and interesting picture of the central issues underlying modern estimation theory and practice. Heuristic, rather than theoretically elegant, arguments are used extensively, with emphasis on physical insights and key questions of practical importance." Numerous illustrative examples, many based on actual applications, have been interspersed throughout the text to lead the student to a concrete understanding of the theoretical material. The inclusion of problems with "built-in" answers at the end of each of the nine chapters further enhances the self-study potential of the text. After a brief historical prelude, the book introduces the mathematics underlying random process theory and state-space characterization of linear dynamic systems. The theory and practice of optimal estimation is them presented, including filtering, smoothing, and prediction. Both linear and non-linear systems, and continuous- and discrete-time cases, are covered in considerable detail. New results are described concerning the application of covariance analysis to non-linear systems and the connection between observers and optimal estimators. The final chapters treat such practical and often pivotal issues as suboptimal structure, and computer loading considerations. This book is an outgrowth of a course given by TASC at a number of US Government facilities. Virtually all of the members of the TASC technical staff have, at one time and in one way or another, contributed to the material contained in the work.
