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# Topic: Estimation theory

###### In the News (Mon 17 Jun 13)

 Estimation Summary Estimating to the 1s makes the equation 2 + 14 – 11 + 8 – 124 – 32 = –143, which is more accurate but more difficult to calculate mentally. A total estimated tip of \$3.75 is close (in fact, an overestimation) to 15 percent of \$24.32, which is \$3.65 (rounded to the nearest cent). Estimation is the calculated approximation of a result which is usable even if input data may be incomplete, uncertain, or noisy. www.bookrags.com /Estimation   (1134 words)

 Cost estimate Estimation theory, more information about Estimation theory The entire purpose of estimation theory is to arrive at an estimator, and preferably an implementable one that could actually be used. An optimal estimator would indicate that all available information in the measured data has been extracted, for if there was unused information in the data then the estimator would not be optimal. In order to arrive at a desired estimator for estimating a single or multiple parameters, it is first necessary to determine a model for the system. www.cost-estimate.biz /Estimation_theory.html   (1079 words)

 Estimation theory - Wikipedia, the free encyclopedia Estimation theory is a branch of statistics and signal processing that deals with estimating the values of parameters based on measured/empirical data. In estimation theory, it is assumed that the desired information is embedded into a noisy signal. In summary, the estimator estimates the parameters of a physical model based on measured data. en.wikipedia.org /wiki/Estimation_theory   (973 words)

 Estimation Theory: Problems   (Site not responding. Last check: 2007-10-26) Estimates for identical parameters are heavily dependent on the assumed underlying probability densities. Although the maximum likelihood estimation procedure was not clearly defined until early in the 20th century, Gauss showed in 1905 that the Gaussian density 1 was the sole density for which the maximum likelihood estimate of the mean equaled the sample average. In this example, we derived the maximum likelihood estimate of the mean and variance of a Gaussian random vector. cnx.rice.edu /content/m11221/latest   (1663 words)

 Amazon.fr : Lessons in Estimation Theory for Signal Processing, Communications, and Control: Livres en anglais: Jerry ...   (Site not responding. Last check: 2007-10-26) Estimation theory is widely used in many branches of science and engineering. It is meant to be an introduction to the general field of estimation theory and, as such, is not encyclopedic in content or in references. In Lesson 2 we show that state estimation is a special case of parameter estimation; i.e., it is the problem of estimating random parameters when these parameters change from one time instant to the next. www.amazon.fr /Lessons-Estimation-Processing-Communications-Control/dp/0131209817   (1568 words)

 Himmelsohn’s General Estimation Theory   (Site not responding. Last check: 2007-10-26) Relative errors are used only for equalities of exact values to their approximations and become indefinite and inadequate if the ratio of the sides of the equality is not close to 1. Estimation methods must be based on the intercommunicated concepts of sets and numbers. But the Cantor's concept [3, 4] of sets ignores multiplicities of their elements, identifies essentially different sets, and gives indefinite numbers of elements and other functions of sets especially if they involve closely spaced elements not exactly known, although the multiplicities of solutions of polynomials are used in algebra. www.tbns.net /leo/GEstiTxt.htm   (1470 words)

 ECE 734 The focus of this course is on linear estimation theory and its applications. We begin with estimation of individual signal vectors, and proceed to estimation of discrete-time scalar and vector signals. Estimation of deterministic as well as random signals is studied. ece.gmu.edu /~yephraim/Courses/ece734/ece734.htm   (436 words)

 math lessons - Density estimation In probability and statistics, density estimation is the construction of an estimate, based on observed data, of an unobservable underlying probability density function. The unobservable density function is thought of as the density according to which a large population is distributed; the data are usually thought of as a random sample from that population. A variety of approaches to density estimation are used, including Parzen windows and a range of data clustering techniques. www.mathdaily.com /lessons/Density_estimation   (99 words)

 SchemeQuest - SCEplus COCOMO Software Cost Estimation Modeling Tool The word "constructive" refers to the fact that the model helps an estimator better understand the complexities of the software job to be done, and by its openness permits the estimator to know exactly why the model gives the estimate it does. However, it is also irresponsible to use FP as your general sizing metric for estimation, as pure FP will give you the same cost (or schedule or quality) estimate for a program with the same functionality developed using different language levels, which is clearly wrong. COCOMO 81 is a model that allows one to estimate the cost, effort, and schedule when planning a new software development activity, according to software development practices that were commonly used in the 1970s through the 1980s. www.schemequest.com   (1867 words)

 Detection and Estimation Theory   (Site not responding. Last check: 2007-10-26) Theory of hypothesis testing, Neyman-Pearson and most powerful tests, uniformly and locally most powerful tests (UMP, LMP), Bayes tests. Likelihood ratio tests, detection of known signals in white and colored Gaussian noise, matched-filter, multi-channel signal detection. Helstrom, Elements of Signal Detection and Estimation, Prentice Hall, 1995. www.ee.bgu.ac.il /~det/DETintro.htm   (176 words)

 Fundamentals of Statistical Signal Processing: Estimation Theory, Volume 1 The outgrowth of a one-semester graduate level course on estimation theory given at the U. of Rhode Island, this text strikes a balance between the highly theoretical expositions written by statisticians and the practical treatments contributed by the many users of applied statistics. The primary focus is on obtaining optimal estimation algorithms that may be implemented on a digital computer. The background assumed is exposure to the basic theory of digital signal processing, probability and random processes, and linear and matrix algebra. www.booksmatter.com /b0133457117.htm   (192 words)

 Estimation - Wikipedia, the free encyclopedia Estimation is the calculated approximation of a result which is usable even if input data may be incomplete, uncertain, or noisy. In mathematics, approximation or estimation typically means finding upper or lower bounds of a quantity that cannot readily be computed precisely. for engineering), accurate estimates are the basis of sound project planning. en.wikipedia.org /wiki/Estimation   (139 words)

 Scrooble.com for : Estimation Theory You probably were looking for the word: estimation theory The common misspelling of the word estimation theory are shown below. I don't think there is an estimation that the American audience isn't savvy... www.scrooble.com /mathematics/estimation-theory.htm   (99 words)

 Optimal State Estimation   (Site not responding. Last check: 2007-10-26) Although the book is firmly grounded in mathematical theory, the approaches that are presented are all given with the goal of eventual implementation in software. The goal of this text is to present state estimation theory in the most clear yet rigorous way possible, while providing enough advanced material and references so that the reader is prepared to contribute new material to the state of the art. Often an engineer needs to estimate the system states because those states are interesting in their own right. academic.csuohio.edu /simond/estimation   (1101 words)

 TMIP|Clearinghouse|Towards Consistent Travel Demand Estimation in Transportation Planning: A Guide to the Theory and ...   (Site not responding. Last check: 2007-10-26) However, none of the models offer a statistical theory for the combined distribution of the parameters; this is required for developing an efficient simultaneous estimation procedure. A possible theoretical foundation for this theory and procedure may be derived using the entropy-maximizing framework of Wilson (1967, 1974). The difficulty in combined travel demand parameter estimation is that travel costs, a key component of the system, are endogenous to the model (see Anas 1988 for a related discussion). tmip.fhwa.dot.gov /clearinghouse/docs/miller/sec9.stm   (4289 words)

 Detection, Estimation, and Modulation Theory, Part I   (Site not responding. Last check: 2007-10-26) Detection, Estimation, and Modulation Theory, Part I, by Harry L. Van Trees, John Wiley and Sons, 1968. It introduced the terms “Detection and Estimation Theory” into the engineering lexicon and led to the introduction of graduate courses at universities throughout the world. When Dr. Bell and I have taught Detection and Estimation Theory in recent years we have augmented the material in the text and incorporated additional problems. gunston.gmu.edu /demt/demtp1/index.htm   (411 words)

 Amazon.ca: Optimal Estimation of Dynamic Systems: Books: John L. Crassidis,John L. Junkins   (Site not responding. Last check: 2007-10-26) A nice feature of this book is that it makes the effort to explain the underlying principles behind the formula for each algorithm; the relationship between different algorithms is equally well addressed. Optimal Estimation of Dynamic Systems explores topics that are important in the field of control where the signals received are used to determine highly sensitive processes such as the flight path of a plane, the orbit of a space vehicle, or the control of a machine. It presents the fundamentals of state estimation theory and the tools for the design of state-of-the-art algorithms for navigation and tracking, vehicle attitude determination. www.amazon.ca /Optimal-Estimation-Dynamic-Systems-Crassidis/dp/158488391X   (477 words)

 EE 538 Detection and Estimation Theory   (Site not responding. Last check: 2007-10-26) Goals: The goal of this course is to introduce the students to the basic theory of signal detection and estimation. Detection, Estimation and Modulation Theory, Part I, Harry L. Van Trees, John Wiley and Sons, Inc., 1968. Lessons in Estimation Theory for Signal Processing, Communications, and Control, Jerry M. Mendel, Prentice Hall, PTR, 1995. www.iyte.edu.tr /~mustafaaltinkaya/EE538/ee538syl-s05.html   (183 words)

 NEWCOM Autumn School - Estimation Theory for wireless communications : new trends (ESTHETE) - October 2005 The purpose of this school is to provide the standard and new tools of the estimation theory used in practical estimation issues encountered in wireless communications. We will focus on the propagation channel estimation as well as on the synchronization issues. We expect that the attendees have a solid background in statistics and probability (random process, different types of convergence, central-limit theorem) as well as in digital communications (linear modulations, detection theory, multi-carrier transmissions). www.comelec.enst.fr /~ciblat/summer_school   (433 words)

 Expert: Dynamic Systems: Mathematical Modeling, Analysis, Simulation, Control Expert He has designed and taught courses on estimation theory and uses the optimal stochastic estimation and control method on a regular basis. He has done definitive work in making parameter estimation by repeated integration maximally accurate, exposing and remedying many of the principal sources of error. He is experienced in applying the so-called maximum likelihood parameter estimation theory. www.intota.com /viewbio.asp?bioID=765452&perID=721942   (862 words)

 ECE 561 - Detection and Estimation Theory Introduction to detection and estimation theory, with applications to communication, control, and radar systems; decision-theory concepts and optimum-receiver principles; detection of random signals in noise, coherent and noncoherent detection; and parameter estimation, linear and nonlinear estimation, and filtering. Signal detection in continuous time (basic theory): Radon-Nikodym derivatives; Grenander's theorem; Karhunen-Loeve expansions and Mercer's theorem; Pitcher's theorem, detection of nonrandom and random signals in Gaussian noise H.V. Poor, An Introduction to Signal Detection and Estimation, Springer-Verlag, 1994. www.ece.uiuc.edu /courses/coursedes.asp?561   (195 words)

 Probability Theory   (Site not responding. Last check: 2007-10-26) Stat 426 continues with discrete and continuous probability distributions, multivariate normal distribution, sampling distributions, the theory of estimation, and hypothesis testing. Stat 427 is an investigation of statistical theory, including the topics of hypothesis testing, Bayesian inference, regression, and sequential analyses. be able to apply the theory of transformations to the sums of independent random variables. www.calpoly.edu /~math/ugcourses/probability.html   (285 words)

 Model Estimation Theory This disparity is not true for the estimator of the arithmetic mean, the numerator of that quantity being the sum of n observations and the denominator being n. The estimator for sigma squared which has n in the denominator is biased for sigma squared (in fact, it always underestimates sigma squared). Least square estimators minimize the square of the deviation between the observed value of y and the fitted or predicted value of y. www.sph.uth.tmc.edu /course/Biometry/LMoye/PH1820-21/PH1820/lectreg2.htm   (646 words)

 Estimation Theory Objectives: Estimation theory is widely used in many branches of science and engineering and there exists a rich collection of estimation methods and algorithms from which to choose. Approximately one half of the course is devoted to parameter estimation while the other half concentrates on state estimation. State estimation: prediction, filtering (the Kalman filter), the steady-state Kalman filter and its relationship to a digital Wiener filter, smoothing. lawww.epfl.ch /page4233.html   (161 words)

 McGraw-Hill AccessScience: Estimation theory Some of the important applications of estimation theory are found in control and communication systems, where it is used to estimate the unknown states and parameters of the system. For example, the position and velocity of a satellite is estimated from ground radar observations of its range, elevation, and azimuth. Generally, the random noise is assumed to have a gaussian distribution, and its mean and covariance may be known or unknown. dx.doi.org /10.1036/1097-8542.242500   (236 words)

 Wiley::An Engineering Approach to Optimal Control and Estimation Theory Sidestepping the realm of theoretical mathematics, An Engineering Approach to Optimal Control and Estimation Theory offers realistic and workable solutions that can be put to immediate use by electrical and mechanical engineers in aerospace and in many other applications. Optimal control and estimation theory is crucial to the design of modern control systems; for instance, navigation, mobile robotics, or automated vehicles and aircraft. An Engineering Approach to Optimal Control and Estimation Theory is an invaluable, self-contained reference for practicing engineers, a useful text for graduate students and qualified senior undergraduates, and an important resource for anyone interested in the future of modern control technology. www.wiley.com /WileyCDA/WileyTitle/productCd-0471121266,descCd-description.html   (605 words)

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