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Thursday, October 8, 2020 | History

1 edition of Fixed Interval Smoothing for State Space Models found in the catalog.

Fixed Interval Smoothing for State Space Models

by Howard L. Weinert

  • 356 Want to read
  • 19 Currently reading

Published by Springer US in Boston, MA .
Written in English

    Subjects:
  • Engineering,
  • Statistics,
  • Computer engineering

  • About the Edition

    Fixed-interval smoothing is a method of extracting useful information from inaccurate data. It has been applied to problems in engineering, the physical sciences, and the social sciences, in areas such as control, communications, signal processing, acoustics, geophysics, oceanography, statistics, econometrics, and structural analysis. This monograph addresses problems for which a linear stochastic state space model is available, in which case the objective is to compute the linear least-squares estimate of the state vector in a fixed interval, using observations previously collected in that interval. The author uses a geometric approach based on the method of complementary models. Using the simplest possible notation, he presents straightforward derivations of the four types of fixed-interval smoothing algorithms, and compares the algorithms in terms of efficiency and applicability. Results show that the best algorithm has received the least attention in the literature. Fixed Interval Smoothing for State Space Models: includes new material on interpolation, fast square root implementations, and boundary value models; is the first book devoted to smoothing; contains an annotated bibliography of smoothing literature; uses simple notation and clear derivations; compares algorithms from a computational perspective; identifies a best algorithm. Fixed Interval Smoothing for State Space Models will be the primary source for those wanting to understand and apply fixed-interval smoothing: academics, researchers, and graduate students in control, communications, signal processing, statistics and econometrics.

    Edition Notes

    Statementby Howard L. Weinert
    SeriesThe Springer International Series in Engineering and Computer Science -- 609, International series in engineering and computer science -- 609.
    Classifications
    LC ClassificationsTK1-9971
    The Physical Object
    Format[electronic resource] /
    Pagination1 online resource (x, 119 pages).
    Number of Pages119
    ID Numbers
    Open LibraryOL27039013M
    ISBN 101461356806, 1461516919
    ISBN 109781461356806, 9781461516910
    OCLC/WorldCa852788122

    StructTS() and tsSmooth() from {stats}: Fit a structural model for a time series by maximum likelihood. Performs fixed-interval smoothing on a univariate time series via a state-space model. Fixed-interval smoothing gives the best estimate of the state at each time point based on the whole observed series. CSE, Penn State Robert Collins Summary about Convolution Computing a linear operator in neighborhoods centered at each pixel. Can be thought of as sliding a kernel of fixed coefficients over the image, and doing a weighted sum in the area of overlap. things to take note of: full: compute a value for any overlap between kernel and imageFile Size: 1MB.

    We present a state-space generalized linear model (SS-GLM) for characterizing neural spiking activity in multiple trials. We estimate the model parameters by maximum likelihood using an approximate Expectation-Maximization (EM) algorithm which employs a recursive point process filter, fixed-interval smoothing and state-space covariance by: 1. This paper gives a detailed overview of the current state of research in relation to the use of state space models and the Kalman-filter in the field of stochastic claims reserving. Most of these state space representations are matrix-based, which complicates their applications. Therefore, to facilitate the implementation of state space models in practice, we present a scalar state space Cited by: 5.

    Fixed Interval Smoothing smooths the estimate x^,,^-^ o\er the time interval from to K where K is fixed and A varies from to K. This thesis uses a fixed-interval smoothing algorithm [Ref p. ]. to smooth the state estimates of the extended Kalman filter in . Gaussian state space models are given in [26]. In order to analyze and make inference about a dynamic This holds if is fixed (fixed-lag smoothing, if a batch of data are considered and (fixed-interval smoothing), or if the state at a particular time is of interest is fixed (fixed-point.


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Fixed Interval Smoothing for State Space Models by Howard L. Weinert Download PDF EPUB FB2

Fixed Interval Smoothing for State Space Models will be the primary source for those wanting to understand and apply fixed-interval smoothing: academics, researchers, and graduate students in control, communications, signal processing, statistics and by: Fixed Interval Smoothing for State Space Models will be the primary source for those wanting to understand and apply fixed-interval smoothing: academics, researchers, and graduate students in control, communications, signal processing, statistics and : Springer US.

Fixed-interval smoothing is a method of extracting useful information from inaccurate data. It has been applied to problems in engineering, the physical sciences, and the social sciences, in areas such as control, communications, signal processing, acoustics, geophysics, oceanography, statistics, econometrics, and structural s: 0.

Fixed Interval Smoothing for State Space Models will be the primary source for those wanting to understand and apply fixed-interval smoothing: academics, researchers, and. Fixed interval smoothing for state-space models [Book Review] Article in IEEE Transactions on Automatic Control 46(12) - January with 8 Reads How we measure 'reads'.

Book reviews: Fixed-interval smoothing for state-space models Article in Automatica 39(9) September with 3 Reads How we measure 'reads'.

Fixed Interval Smoothing for State Space Models will be the primary source for those wanting to understand and apply fixed-interval smoothing: academics, researchers, and graduate students in control, communications, signal processing, statistics and econometrics.

Get this from a library. Fixed Interval Smoothing for State Space Models. [Howard L Weinert] -- Fixed-interval smoothing is a method of extracting useful information from inaccurate data.

It has been applied to problems in engineering, the physical sciences, and the social sciences, in. Fixed Interval Smoothing for State Space Models will be the primary source for those wanting to understand and apply fixed-interval smoothing: academics, researchers, and graduate students in control, communications, signal processing, statistics and Springer International Engineering and Computer Science: Fixed Interval.

Fixed Interval Smoothing for State Space Models by Howard Weinert starting at $ Fixed Interval Smoothing for State Space Models has 2 available editions. In fixed-interval smoothing we seek an estimate of the state at some of the interior points of the time interval. During the smoothing process we do not obtain any new measurements.

Section discusses the forward-backward approach to smoothing, which is perhaps the most straightforward smoothing algorithm. “It is possible to employ a single smoothing scheme, based on fixed-interval smoothing to solve all three problems” [1]. “A state is said to be smoothable if an optimal smoother provides a state estimate superior to that obtained when the final optimal filter estimate is extrapolated backwards in time” [4].File Size: KB.

Modelling and Application of Stochastic Processes - Ebook written by Uday B. Desai. Read this book using Google Play Books app on your PC, android, iOS devices.

Download for offline reading, highlight, bookmark or take notes while you read Modelling and Application of.

Fixed Interval Smoothing for State Space Models by Howard L. Weinert. Springer, Hardcover. Good. Disclaimer:A copy that has been read, but remains in clean condition.

All pages are intact, and the cover is intact. The spine may show signs of wear. Pages can include limited notes and highlighting, and the copy can include previous owner inscriptions. Fixed Interval Smoothing for State Space Models: includes new material on interpolation, fast square root implementations, and boundary value models; is the first book devoted to smoothing; contains an annotated bibliography of smoothing literature; uses simple notation and clear derivations; compares algorithms from a computational perspective.

A discussion on the fixed-interval smoothing problem for different types of the state-space models can be viewed in Weinert ; this book includes a wide list of references on the treatment of the problem and applications of the fixed-interval by: 4.

Weinert HL, Desai UB (). On Complementary Models and Fixed-Interval Smoothing. IEEE Transactions on Automatic Control. 26(4). Weinert HL, Byrd RH, Sidhu GS ().

A stochastic framework for recursive computation of spline functions: Part II, smoothing splines. Journal of Optimization Theory and Applications. 30(2). Cite this chapter as: Weinert H.L.

() Discrete Smoothers. In: Fixed Interval Smoothing for State Space Models. The Springer International Series. —The paper reviews and generalizes recent filtering and smoothing algorithms for observations generated by a state model. In particular the paper discusses the modified Kalman filter derived by Ansley and Kohn () and Kohn and Ansley () to deal with state space models having partially diffuse initial conditions, and shows how to compute the limiting normalized likelihood Cited by: 3.

The conditional expectation is computed by expressing the process in state space form and using the filtering and smoothing results in Ansley and Kohn (Annals Statist., 11 (), pp–). We show how to use our algorithms to estimate the smoothness parameter and how to obtain Bayesian confidence intervals for the unknown function and Cited by:.

FIXED-LAG SMOOTHING. In fixed-lag smoothing we want to obtain an estimate of the state at time (k - N) given measurements up to and including time k, where the time index k continually changes as we obtain new measurements, but the lag N is a other words, at each time point we have N future measurements available for our state estimate.State space model for linear regression with drift 36 Examples of state space models 39 Exercises 46 4 Bayesian filtering equations and exact solutions 51 Probabilistic state space models 51 Bayesian filtering equations 54 Kalman filter 56 v.Ahn, C.

(). New quasi-deadbeat FIR smoother for discrete-time state-space signal models: an LMI approach. IEICE Trans. Fundamentals of Electronics, Communications and Computer Sciences EA(9), Ahn, C. K. & Han, S. H. (). New H_∞ FIR smoother for linear discrete-time state-space models. IEICE Trans.

Commun. EB(3), Author: Seiichi Nakamori.