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Record W186403726

The Oxford Handbook of Nonlinear Filtering

2011· book· en· W186403726 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.984
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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