title: Sensitivity statistical estimates of local aposterior inference matrix-vector equations in algebraic Bayesian networks on quanta propositions
metadata.name: dc.contributor.author
metadata.value: Zolotin, Andrey A.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Tulupyev, Aleksandr L.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2018-04-26T09:26:49Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2018-04-26T09:26:49Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2018-03
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Zolotin A.A., Tulupyev A. L. Sensitivity statistical estimates of local aposterior inference matrix-vector equations in algebraic Bayesian networks on quanta propositions. Vestnik SPbSU. Mathematics. Mechanics. Astronomy, 2018, vol. 5 (63), issue 1, pp. 60–69.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: 10.21638/11701/spbu01.2018.107
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/9499
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: An approach to the sensitivity analysis of local aposterior inference equations in algebraic
  Bayesian networks is proposed in the paper. Basic definitions and formulations are briefly
  given and the development of the matrix-vector approach of a posterior inference is
  considered. The propagation of deterministic and stochastic evidences in a knowledge pattern
  with scalar estimates of probabilities of truth over quantum propositions is described. For
  each of the provided cases necessary metrics are introduced and transformations which
  result into construction of 4 linear programming problems which solution gives the required
  estimates are performed. In addition, 2 theorems that postulate the covering estimates for
  both cases are formulated. The results obtained in the paper prove the correctness of models
  and create a basis for investigation of local and global logic-probabilistic inference equations
  sensitivity.
metadata.lang: en_GB
metadata.name: dc.description.sponsorship
metadata.value: Часть публикуемых материалов получена в рамках проектов, выполненных по госзаданию СПИИРАН №0073-2014-0002 и при финансовой поддержке РФФИ (гранты №15-01-09001-а,
  №18-01-00626-а).
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Mathematics. Mechanics. Astronomy;Volume 5(63); Issue 1
metadata.lang: -
metadata.name: dc.subject
metadata.value: uncertain knowledge
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: evidence propagation
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: probabilistic logic
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: algebraic Bayesian networks
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: probabilistic-logic inference
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: sensitivity statistical estimate
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: probabilistic graphical model
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: matrix-vector equations
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Sensitivity statistical estimates of local aposterior inference matrix-vector equations in algebraic Bayesian networks on quanta propositions
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 07-Zolotin.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/9499/1/07-Zolotin.pdf
resources.format: Adobe PDF
resources.size: 320,23 kB