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A Noninformative Prior for Neural Networks [PDF]
Machine Learning, 2003 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Statistica, 2021 We consider here the generalization of the Bilal distribution proposed by Abd-Elrahman (2017) by zeroing in on two measures of reliability, R(t) and P, based on type II censoring. We obtain point estimators namely, λ and θ, of the above said distribution,
Ajit Chaturvedi +2 more
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On the implementation of local probability matching priors for interest parameters [PDF]
, 2005 Probability matching priors are priors for which the posterior probabilities of certain specified sets are exactly or approximately equal to their coverage probabilities.
Sweeting, TJ, Trevor J. Sweeting
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Journal of Statistical Theory and Applications (JSTA), 2020 The output of an engineering process is the result of several inputs, which may be homogeneous or heterogeneous and to study them, we need a model which should be flexible enough to summarize efficiently the nature of such processes.
Muhammad Tahir +4 more
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Incorporating prior knowledge into Bayesian models for genetic evaluation in soybean breeding [PDF]
Pesquisa Agropecuária BrasileiraThe objective of this work was to compare the use of noninformative and informative priors in Bayesian models, as well as to evaluate the viability of including informative priors in the estimation of variance components and genetic values in soybean ...
Jeniffer Santana Pinto Coelho Evangelista +6 more
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Bagging statistical network inference from large-scale gene expression data. [PDF]
PLoS ONE, 2012 Modern biology and medicine aim at hunting molecular and cellular causes of biological functions and diseases. Gene regulatory networks (GRN) inferred from gene expression data are considered an important aid for this research by providing a map of ...
Ricardo de Matos Simoes +1 more
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Hilbert–Schmidt separability probabilities and noninformativity of priors [PDF]
Journal of Physics A: Mathematical and General, 2006 The Horodecki family employed the Jaynes maximum-entropy principle, fitting the mean (b_{1}) of the Bell-CHSH observable (B). This model was extended by Rajagopal by incorporating the dispersion (σ_{1}^2) of the observable, and by Canosa and Rossignoli, by generalizing the observable (B_α).
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Noninformative priors for the log-logistic distribution [PDF]
Journal of the Korean Data and Information Science Society, 2014 Abstract In this paper, we develop the noninformative priors for the scale parameter andthe shape parameter in the log-logistic distribution. We developed the rst and secondorder matching priors. It turns out that the second order matching prior matches the al-ternative coverage probabilities, and is a highest posterior density matching prior.
Sang Gil Kang, Dal Ho Kim, Woo Dong Lee
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A Family of Bayesian Estimators for the Two-Parametric Burr Type II Distribution
Journal of Function Spaces, 2022 This study discusses the posterior estimation for the parameters of the Burr type II distribution (BIID). The informative and noninformative priors along with different loss functions have also been assumed for the posterior estimation. The applicability
R. Alshenawy +4 more
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Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits
CoRR, 2023 In the stochastic multi-armed bandit problem, a randomized probability matching policy called Thompson sampling (TS) has shown excellent performance in various reward models. In addition to the empirical performance, TS has been shown to achieve asymptotic problem-dependent lower bounds in several models.
Jongyeong Lee +3 more
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