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Observation Noise-Gain Detection for Markov Chains Observed through scaled Brownian Motion

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dc.contributor.authorBensoussan, Alain
dc.contributor.authorMalcolm, William
dc.coverage.spatialCancun Mexico
dc.date.accessioned2015-12-07T22:41:27Z
dc.date.available2015-12-07T22:41:27Z
dc.date.createdDecember 9-11 2008
dc.date.issued2008
dc.date.updated2016-02-24T11:54:47Z
dc.description.abstractIn this preliminary article we consider the problem of estimating an unknown noise-gain for a Markov chain observed through a scaled Brownian motion. It is assumed that the unknown noise-gain is time invariant. Two objectives are addressed in this work, 1) compute an estimation scheme that is fast, and 2) compute an estimation scheme without recourse to stochastic integration. To address the first objective we avoid the Expectation Maximization (EM) algorithm, instead we develop an estimation scheme for a finite number of candidate model hypotheses. To address the second objective we develop a version of the Gauge-Transformation technique introduced by J. M. C. Clark.
dc.identifier.isbn9781424431243
dc.identifier.urihttp://hdl.handle.net/1885/24328
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)
dc.relation.ispartofseriesIEEE Conference on Decision and Control 2008
dc.sourceProceedings of IEEE Conference on Decision and Control 2008
dc.subjectKeywords: Detection; Filtering; Martingales; Reference probability; Wonham filter; Brownian movement; Markov processes; Estimation Detection; Filtering; Martingales; Reference probability; Wonham filter
dc.titleObservation Noise-Gain Detection for Markov Chains Observed through scaled Brownian Motion
dc.typeConference paper
local.contributor.affiliationBensoussan, Alain, University of Texas
local.contributor.affiliationMalcolm, William, College of Physical and Mathematical Sciences, ANU
local.contributor.authoruidMalcolm, William, u3881226
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080202 - Applied Discrete Mathematics
local.identifier.ariespublicationu9209279xPUB31
local.identifier.doi10.1109/CDC.2008.4739239
local.identifier.scopusID2-s2.0-62949233371
local.type.statusPublished Version

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