Presentation 2018-08-27
Bayesian Inference for Field of Physical Quantity from Data obtained at several Locations
Masato Ota, Takeshi Okadome,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) This paper proposes a novel method for estimating the physical quantity at every location (physical quan- tity field) from sensor data obtained at several locations of the environment. We use the low precision sensors that are not calibrated and the high precision sensors that are fewer than those sensors. When the physical quantity field is discontinuous, the estimation accuracy decreases with the existing method. In this paper, A probabilistic generation model is constructed and the posterior probability of the field is obtained by variational Bayes in consideration of the systematic error of the low precision sensors and the discontinuity of the physical quantity field.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Bayesian Inference / Variational Bayes / Unsupervised Learning / Gaussian process
Paper # AI2018-23
Date of Issue 2018-08-20 (AI)

Conference Information
Committee AI
Conference Date 2018/8/27(1days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Tsunenori Mine(Kyushu Univ.)
Vice Chair Daisuke Katagami(Tokyo Polytechnic Univ.) / Naoki Fukuta(Shizuoka Univ.)
Secretary Daisuke Katagami(Ritsumeikan Univ.) / Naoki Fukuta(Univ. of Electro-Comm.)
Assistant Yuko Sakurai(AIST)

Paper Information
Registration To Technical Committee on Artificial Intelligence and Knowledge-Based Processing
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Bayesian Inference for Field of Physical Quantity from Data obtained at several Locations
Sub Title (in English)
Keyword(1) Bayesian Inference
Keyword(2) Variational Bayes
Keyword(3) Unsupervised Learning
Keyword(4) Gaussian process
Keyword(5)
1st Author's Name Masato Ota
1st Author's Affiliation Kwansei Gakuin University(KG Univ.)
2nd Author's Name Takeshi Okadome
2nd Author's Affiliation Kwansei Gakuin University(KG Univ.)
Date 2018-08-27
Paper # AI2018-23
Volume (vol) vol.118
Number (no) AI-197
Page pp.pp.55-60(AI),
#Pages 6
Date of Issue 2018-08-20 (AI)