Variational Bayes via propositionalized probability computation in PRISM

DOI10.1007/s10472-009-9135-8zbMath1178.68591OpenAlexW2093425039MaRDI QIDQ841638

Kenichi Kurihara, Taisuke Sato, Yoshitaka Kameya

Publication date: 18 September 2009

Published in: Annals of Mathematics and Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10472-009-9135-8

Mathematics Subject Classification ID

Bayesian inference (62F15) Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items

Viterbi training in PRISM, Probabilistic (logic) programming concepts, Learning failure-free PRISM programs, Online Bayesian inference for the parameters of PRISM programs, PRISM revisited: declarative implementation of a probabilistic programming language using multi-prompt delimited control, Learning to rank in PRISM, Logic-Based Probabilistic Modeling

Uses Software

• Rose
• Pfam
• PRISM
• AutoClass

Cites Work

• Estimating the dimension of a model
• Case-factor diagrams for structured probabilistic modeling
• Variational Bayesian Grammar Induction for Natural Language
• Variational Bayesian learning of directed graphical models with hidden variables
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