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Statements

Subject Item
n2:RIV%2F67985556%3A_____%2F05%3A00026443%21RIV06-AV0-67985556
rdf:type
n8:Vysledek skos:Concept
dcterms:description
Je ukázána konstrukce apriorní informace pro bayesovskou identifikaci lineárního regresního modelu s normálním šumem a malým počtem dat. Model přestavuje průběh aktivity štítné žlázy po terapeutické aplikaci 131I pacientům s karcinomem štítné žlázy. Pro apriorní informaci je použito omezení definičního oboru parametrů a teorie sdílení znalostí mezi agenty. Apriorní informace zlepšuje predikční schopnost modelu a snižuje dolní hranici pro minimální počet dat, s nimiž lze identifikaci provést. Construction of prior information for Bayes identificationof linear regression model with normal noise and lack of data is presented. The model discrabes accumulation of activity in thyroid gland after administration of 131I to patients with thyroid cancer. One way of prior information is a restriction of parameters support. Second way uses theory of information merging between agents. Prior information improves prediction ability of the model and decreases lower bound for sufficient amount of data. Construction of prior information for Bayes identificationof linear regression model with normal noise and lack of data is presented. The model discrabes accumulation of activity in thyroid gland after administration of 131I to patients with thyroid cancer. One way of prior information is a restriction of parameters support. Second way uses theory of information merging between agents. Prior information improves prediction ability of the model and decreases lower bound for sufficient amount of data.
dcterms:title
Prior information in Bayesian identification of a linear regression model Prior information in Bayesian identification of a linear regression model Apriorní informace v bayesovké identifikaci lineárního regresního modelu
skos:prefLabel
Apriorní informace v bayesovké identifikaci lineárního regresního modelu Prior information in Bayesian identification of a linear regression model Prior information in Bayesian identification of a linear regression model
skos:notation
RIV/67985556:_____/05:00026443!RIV06-AV0-67985556
n3:strany
1;6
n3:aktivita
n4:P n4:Z
n3:aktivity
P(1ET100750404), P(1M0572), Z(AV0Z10750506)
n3:dodaniDat
n17:2006
n3:domaciTvurceVysledku
n15:6585256 n15:7450133
n3:druhVysledku
n19:D
n3:duvernostUdaju
n11:S
n3:entitaPredkladatele
n14:predkladatel
n3:idSjednocenehoVysledku
538500
n3:idVysledku
RIV/67985556:_____/05:00026443
n3:jazykVysledku
n18:eng
n3:klicovaSlova
fictiticous data; information matrix; Gauss-inverse-Wishart; MCMC
n3:klicoveSlovo
n13:MCMC n13:Gauss-inverse-Wishart n13:fictiticous%20data n13:information%20matrix
n3:kontrolniKodProRIV
[FE65C59882FA]
n3:mistoKonaniAkce
Izola
n3:mistoVydani
Ljubljana
n3:nazevZdroje
Proceedings of the 6th International PhD Workshop in Systems and Control, Young Generation Viewpoint
n3:obor
n16:BB
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
3
n3:projekt
n5:1M0572 n5:1ET100750404
n3:rokUplatneniVysledku
n17:2005
n3:tvurceVysledku
Varga, F. Kárný, Miroslav Jirsa, Ladislav
n3:typAkce
n6:EUR
n3:zahajeniAkce
2005-10-04+02:00
n3:zamer
n12:AV0Z10750506
s:numberOfPages
6
n9:hasPublisher
Jozef Stefan Institute
n21:isbn
961-6303-74-0