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Statements

Subject Item
n2:RIV%2F67985556%3A_____%2F13%3A00396771%21RIV14-GA0-67985556
rdf:type
n3:Vysledek skos:Concept
dcterms:description
To successfully learn from the information provided by avail- able information sources, the choice of automatic method combining them into one aggregate result plays an important role. To respect the reliability in the source’s performance each of them is assigned a weight, often subjectively influenced. To overcome this issue, we briefly describe the method based on Bayesian decision theory and elements of infor- mation theory. In particular we consider discrete-type information, rep- resented by probability mass functions (pmfs) and obtain an aggregate result, which has also form of pmf. This result of decision making pro- cess is found to be a weighted linear combination of available information. Besides the brief description of the novel method, the paper focuses on its comparison with other combination methods. Since we consider the available information and unknown aggregate as pmfs, we mainly focus on the case when the parameter of binomial distribution is of interest and the sources provide appropriate pmfs. To successfully learn from the information provided by avail- able information sources, the choice of automatic method combining them into one aggregate result plays an important role. To respect the reliability in the source’s performance each of them is assigned a weight, often subjectively influenced. To overcome this issue, we briefly describe the method based on Bayesian decision theory and elements of infor- mation theory. In particular we consider discrete-type information, rep- resented by probability mass functions (pmfs) and obtain an aggregate result, which has also form of pmf. This result of decision making pro- cess is found to be a weighted linear combination of available information. Besides the brief description of the novel method, the paper focuses on its comparison with other combination methods. Since we consider the available information and unknown aggregate as pmfs, we mainly focus on the case when the parameter of binomial distribution is of interest and the sources provide appropriate pmfs.
dcterms:title
A note on weighted combination methods for probability estimation A note on weighted combination methods for probability estimation
skos:prefLabel
A note on weighted combination methods for probability estimation A note on weighted combination methods for probability estimation
skos:notation
RIV/67985556:_____/13:00396771!RIV14-GA0-67985556
n3:predkladatel
n4:ico%3A67985556
n5:aktivita
n19:P n19:I
n5:aktivity
I, P(GA13-13502S)
n5:dodaniDat
n9:2014
n5:domaciTvurceVysledku
n7:2844737
n5:druhVysledku
n17:D
n5:duvernostUdaju
n8:S
n5:entitaPredkladatele
n21:predkladatel
n5:idSjednocenehoVysledku
58896
n5:idVysledku
RIV/67985556:_____/13:00396771
n5:jazykVysledku
n12:eng
n5:klicovaSlova
weighting methods; parameter estimation; Kerridge inaccuracy; maximum entropy principle; binomial distribution
n5:klicoveSlovo
n6:Kerridge%20inaccuracy n6:maximum%20entropy%20principle n6:parameter%20estimation n6:binomial%20distribution n6:weighting%20methods
n5:kontrolniKodProRIV
[CFAE8656D87B]
n5:mistoKonaniAkce
Prague
n5:mistoVydani
Prague
n5:nazevZdroje
Preprints of the 3rd International Workshop on Scalable Decision Making held in conjunction with ECML/PKDD 2013
n5:obor
n18:BD
n5:pocetDomacichTvurcuVysledku
1
n5:pocetTvurcuVysledku
1
n5:projekt
n16:GA13-13502S
n5:rokUplatneniVysledku
n9:2013
n5:tvurceVysledku
Sečkárová, Vladimíra
n5:typAkce
n10:WRD
n5:zahajeniAkce
2013-09-23+02:00
s:numberOfPages
12
n15:hasPublisher
Ústav teorie informace a automatizace AV ČR
n13:isbn
978-80-903834-8-7