Restricted Quasi Bayesian Networks as a Prototyping Tool for Computational Models of Individual Cortical Areas

Naoto Takahashi, Yuuji Ichisugi
; Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks, PMLR 73:188-199, 2017.

Abstract

We propose \textit{restricted quasi Bayesian networks} as an efficient prototyping tool for designing computational models of individual cortical areas of the brain. Restricted quasi Bayesian networks are simplified Bayesian networks that only distinguish probability value 0 from other values. Using our tool, it is possible to concentrate on the essential part of model design and efficiently construct prototypes. We demonstrate that restricted quasi Bayesian networks actually work well as a prototyping tool by implementing a syntactic parser for an ambiguous English sentence.

Cite this Paper


BibTeX
@InProceedings{pmlr-v73-takahashi17a, title = {Restricted Quasi Bayesian Networks as a Prototyping Tool for Computational Models of Individual Cortical Areas}, author = {Naoto Takahashi and Yuuji Ichisugi}, booktitle = {Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks}, pages = {188--199}, year = {2017}, editor = {Antti Hyttinen and Joe Suzuki and Brandon Malone}, volume = {73}, series = {Proceedings of Machine Learning Research}, month = {20--22 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v73/takahashi17a/takahashi17a.pdf}, url = {http://proceedings.mlr.press/v73/takahashi17a.html}, abstract = {We propose \textit{restricted quasi Bayesian networks} as an efficient prototyping tool for designing computational models of individual cortical areas of the brain. Restricted quasi Bayesian networks are simplified Bayesian networks that only distinguish probability value 0 from other values. Using our tool, it is possible to concentrate on the essential part of model design and efficiently construct prototypes. We demonstrate that restricted quasi Bayesian networks actually work well as a prototyping tool by implementing a syntactic parser for an ambiguous English sentence.} }
Endnote
%0 Conference Paper %T Restricted Quasi Bayesian Networks as a Prototyping Tool for Computational Models of Individual Cortical Areas %A Naoto Takahashi %A Yuuji Ichisugi %B Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks %C Proceedings of Machine Learning Research %D 2017 %E Antti Hyttinen %E Joe Suzuki %E Brandon Malone %F pmlr-v73-takahashi17a %I PMLR %J Proceedings of Machine Learning Research %P 188--199 %U http://proceedings.mlr.press %V 73 %W PMLR %X We propose \textit{restricted quasi Bayesian networks} as an efficient prototyping tool for designing computational models of individual cortical areas of the brain. Restricted quasi Bayesian networks are simplified Bayesian networks that only distinguish probability value 0 from other values. Using our tool, it is possible to concentrate on the essential part of model design and efficiently construct prototypes. We demonstrate that restricted quasi Bayesian networks actually work well as a prototyping tool by implementing a syntactic parser for an ambiguous English sentence.
APA
Takahashi, N. & Ichisugi, Y.. (2017). Restricted Quasi Bayesian Networks as a Prototyping Tool for Computational Models of Individual Cortical Areas. Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks, in PMLR 73:188-199

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