T
Toshio Ohnishi
Researcher at Kyushu University
Publications - 21
Citations - 82
Toshio Ohnishi is an academic researcher from Kyushu University. The author has contributed to research in topics: Conjugate prior & Bayesian probability. The author has an hindex of 5, co-authored 21 publications receiving 73 citations. Previous affiliations of Toshio Ohnishi include Graduate University for Advanced Studies.
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Bayesian prediction of a density function in terms of e-mixture
Takemi Yanagimoto,Toshio Ohnishi +1 more
TL;DR: In this article, a dualistic structure is observed between the proposed predictor and the optimum predictor under the m -divergence loss, the latter of which is dominantly discussed in the existing literature.
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Search designs for 2m factorials derived from balanced arrays of strength 2(l+1) and ad-optimal search designs
TL;DR: In this paper, a search design for the 2m type such that at most knonnegative effects can be searched among (l+1)-factor interactions and estimated along with the effects up to l- factor interactions, provided (l + 1)-factor and higher order interactions are negligible except for the k effects.
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Saddlepoint condition on a predictor to reconfirm the need for the assumption of a prior distribution
Takemi Yanagimoto,Toshio Ohnishi +1 more
TL;DR: In this article, saddlepoint conditions on a predictor are introduced and developed to reconfirm the need for the assumption of a prior distribution in constructing a useful inferential procedure, which indicates the promising role of Bayesian criteria, such as the deviance information criterion (DIC).
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Extensions of the conjugate prior through the Kullback-Leibler separators
Takemi Yanagimoto,Toshio Ohnishi +1 more
TL;DR: In this paper, the conjugate prior for the exponential family, referred to also as the natural conjugates, is represented in terms of the Kullback-Leibler separator.
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Standardized posterior mode for the flexible use of a conjugate prior
Takemi Yanagimoto,Toshio Ohnishi +1 more
TL;DR: In this paper, the posterior mode under the standardized prior density is proposed to estimate a mean (vector) parameter, and its potential usefulness is discussed, and the treatment of this treatment makes the choice of a prior density flexible.