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  • 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. (en)
Title
  • A note on weighted combination methods for probability estimation
  • A note on weighted combination methods for probability estimation (en)
skos:prefLabel
  • A note on weighted combination methods for probability estimation
  • A note on weighted combination methods for probability estimation (en)
skos:notation
  • RIV/67985556:_____/13:00396771!RIV14-GA0-67985556
http://linked.open...avai/riv/aktivita
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  • I, P(GA13-13502S)
http://linked.open...vai/riv/dodaniDat
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  • 58896
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  • RIV/67985556:_____/13:00396771
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  • weighting methods; parameter estimation; Kerridge inaccuracy; maximum entropy principle; binomial distribution (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [CFAE8656D87B]
http://linked.open...v/mistoKonaniAkce
  • Prague
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  • Prague
http://linked.open...i/riv/nazevZdroje
  • Preprints of the 3rd International Workshop on Scalable Decision Making held in conjunction with ECML/PKDD 2013
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http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Sečkárová, Vladimíra
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
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  • Ústav teorie informace a automatizace AV ČR
https://schema.org/isbn
  • 978-80-903834-8-7
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