Results 31 to 40 of about 1,280,497 (284)
How Bayesian should Bayesian optimisation be? [PDF]
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021 Bayesian optimisation (BO) uses probabilistic surrogate models - usually Gaussian processes (GPs) - for the optimisation of expensive black-box functions. At each BO iteration, the GP hyperparameters are fit to previously-evaluated data by maximising the marginal likelihood.
De Ath, George +2 more
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The American Statistician, 2019 Although no universally accepted definition of causality exists, in practice one is often faced with the question of statistically assessing causal relationships in different settings. We present a uniform general approach to causality problems derived from the axiomatic foundations of the Bayesian statistical framework.
Pierre Baldi, Babak Shahbaba
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Computing for Numeracy: How Safe is Your COVID-19 Social Bubble?
Numeracy, 2021 The COVID-19 pandemic has led many people to form social bubbles. These social bubbles are small groups of people who interact with one another but restrict interactions with the outside world.
Charles Connor
doaj +1 more source
Bayesian games with a continuum of states [PDF]
, 2017 We show that every Bayesian game with purely atomic types has a measurable Bayesian equilibrium when the common knowl- edge relation is smooth. Conversely, for any common knowledge rela- tion that is not smooth, there exists a type space that
Hellman, Ziv, Levy, Yehuda John
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Machine Learning: Science and Technology, 2023 Abstract In this note we present a fully information theoretic approach to renormalization inspired by Bayesian statistical inference, which we refer to as Bayesian renormalization. The main insight of Bayesian renormalization is that the Fisher metric defines a correlation length that plays the role of an emergent renormalization group (
David S Berman +2 more
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Statistical Theory and Related Fields, 2019 Demographic estimation becomes a problem of small area estimation when detailed disaggregation leads to small cell counts. The usual difficulties of small area estimation are compounded when the available data sources contain measurement errors.
Junni L. Zhang, John Bryant
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Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings [PDF]
, 2011 Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty.
A Quinn +49 more
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Bayesian threshold analysis of litter size in sheep
Revista Colombiana de Ciencias Pecuarias, 2022 Background: Litter size at birth (LSB) is one of the most important economic traits in sheep and could be used in genetic improvement schemes for meat production.
Amin Mortazavi +5 more
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The myths and realities of Bayesian chronological modeling revealed [PDF]
, 2017 We review the history of Bayesian chronological modeling in archaeology and demonstrate that there has been a surge over the past several years in American archaeological applications.
Hamilton, W. Derek, Krus, Anthony M.
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Journal of Epidemiology, 2023 Background: Tracking gestational weight gain (GWG) during pregnancy makes it possible to optimize pregnancy outcomes, and GWG growth curves are well suitable for this purpose. The GWG guidelines for Japanese were revised in 2021.
Naho Morisaki +13 more
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