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Record W186403726

The Oxford Handbook of Nonlinear Filtering

2011· book· en· W186403726 on OpenAlexaboutno aff
Dan Crisan, B. L. Rozovskiĭ

Bibliographic record

VenueRePEc: Research Papers in Economics · 2011
Typebook
Languageen
FieldComputer Science
TopicTarget Tracking and Data Fusion in Sensor Networks
Canadian institutionsnot available
Fundersnot available
KeywordsMathematicsFiltering problemNonlinear systemLipschitz continuityNonlinear filterApplied mathematicsParticle filterBrownian motionStochastic differential equationKalman filterDiscretizationExponential stabilityControl theory (sociology)Filter (signal processing)Mathematical analysisExtended Kalman filterComputer scienceFilter design
DOInot available

Abstract

fetched live from OpenAlex

In many areas of human endeavour, the systems involved are not available for direct measurement. Instead, by combining mathematical models for a system's evolution with partial observations of its evolving state, we can make reasonable inferences about it. The increasing complexity of the modern world makes this analysis and synthesis of high-volume data an essential feature in many real-world problems. The celebrated Kalman-Bucy filter, designed for linear dynamical systems with linearly structured measurements, is the most famous Bayesian filter. Its generalizations to nonlinear systems and/or observations are collectively referred to as nonlinear filtering (NLF), an extension of the Bayesian framework to the estimation, prediction, and interpolation of nonlinear stochastic dynamics. NLF uses a stochastic model to make inferences about an evolving system and is a theoretically optimal algorithm. The breadth of its applications, firmly established and still emerging, is simply astounding. Early uses such as cryptography, tracking, and guidance were mostly of a military nature. Since then, the scope has exploded. It includes the study of global climate, estimating the state of the economy, identifying tumours using non-invasive methods, and much more. The Oxford Handbook of Nonlinear Filtering is the first comprehensive written resource for the subject. It contains classical and recent results and applications, with contributions from 58 authors. Collated into 10 parts, it covers the foundations of nonlinear filtering, connections to stochastic partial differential equations, stability and asymptotic analysis, estimation and control, approximation theory and numerical methods for solving the nonlinear filtering problem (including particle methods). It also contains a part dedicated to the application of nonlinear filtering to several problems in mathematical finance. Contributors to this volume - R. Atar - Department of Electrical Engineering, Technion, Haifa, Israel A. Bensoussan - University of Texas at Dallas, USA H. A. P. Blom - National Aerospace Laboratory NLR, The Netherlands A. Budhiraja - University of North Carolina, USA M. Cakanyldirim - University of Texas at Dallas, USA P. Y. Chigansky - The Weizmann Institute of Science J. M. C. Clark - Imperial College London, UK D. Crisan - Imperial College London, UK M. Davis - Imperial College London, UK A. Doucet - The Institute of Statistical Mathematics, Tokyo, Japan. T. Duncan - University of Kansas, USA R. J. Elliott - University of Calgary, Australia R. Frey - Universitat Leipzig, Leipzig F. Le Gland - IRISA/INRIA, France B. Grigelionis - Lithuania F. Gustaffson - Linkoping University, Sweden M. Hairer - University of Warwick, UK R. Van Handel - Princeton University, USA A. J. Heunis - University of Waterloo, Canada A. M. Johansen - University of Warwick, UK R. Karlsson - Linkoping University, Sweden M. L. Kleptsyna - Universite du Maine, France N. V. Krylov - University of Minnesota, USA H. Kunita - Fukuoka, Japan T. Kurtz - University of Wisconsin- Madison, USA H. Kushner - Brown University, USA R. Lipster - Tel Aviv University, Israel C. Litterer - Mathematical Institute, Oxford, UK T. Lyons - Mathematical Institute, Oxford, UK S. V. Lototsky - University of Southern California, USA M. Chaleyat-Maurel - Universite Paris Descartes 45, Paris Hong Miao - Colorado State University, USA R. Mikulevicius - USC Department of Mathematics, Los Angeles, USA G. Milstein - Ural State University, Russia V. Monbet - Universite de Bretagne-Sud, France P. del Moral - Universite Bordeaux 1, France G. Nappo - University "La Sapienza ", Italy N. J. Newton - University of Essex, UK F. Patras - Universite de Nice, France H. Pham - Universites Paris 6- Paris 7, France B. Rozovski? - Brown University, USA S. Rubenthaler - Universite de Nice, France W. J. Runggaldier - Universita degli Studi di Padova, Italy T.B. Schon - Linkoping University, Sweden L. C. Scott - University of Missouri at Kansas City, USA S. P. Sethi - University of Texas at Dallas, USA Y. Bar-Shalom - University of Connecticut, USA W. Stannat - Fachbereich Mathematik A. Stuart - University of Warwick, UK V.-D. Tran - Universite de Bretagne-Sud, France M. Tretyakov - University of Leicester, UK A. Y. Veretennikov - University of Leeds, UK R. Vinter - Imperial College London, UK J. Voss - University of Warwick, UK Zhenyu Wu - University of Saskatchewan, Canada J. Xiong - Mathematics Department, Knoxville, USA O. Zeitouni - University of Minnesota, USA Y. Zeng - University of Missouri at Kansas City, USA

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.117
Threshold uncertainty score0.390

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.1170.086

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.277
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations153
Published2011
Admission routes1
Has abstractyes

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Same venueRePEc: Research Papers in EconomicsSame topicTarget Tracking and Data Fusion in Sensor NetworksFrench-language works237,207