Results 11 to 20 of about 47,862 (173)
Reflecting About Selecting Noninformative Priors [PDF]
Journal of Applied & Computational Mathematics, 2014 15 pages, 8 figures, 5 ...
Kamary, Kaniav, Robert, Christian P.
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Constrained noninformative priors [PDF]
, 1994 The Jeffreys noninformative prior distribution for a single unknown parameter is the distribution corresponding to a uniform distribution in the transformed model where the unknown parameter is approximately a location parameter. To obtain a prior distribution with a specified mean but with diffusion reflecting great uncertainty, a natural ...
Atwood, C. L.
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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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Noninformative priors for the two sample normal problem
Test, 1996 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ghosh, M., Yang, M.-Ch.
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PsychBayesian estimation of latent variable models provides some unique advantages to researchers working with small samples and complex models when compared with the more commonly used maximum likelihood approach.
Holmes Finch
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Default Priors in a Zero-Inflated Poisson Distribution: Intrinsic Versus Integral Priors
MathematicsPrior elicitation is an important issue in both subjective and objective Bayesian frameworks, where prior distributions impose certain information on parameters before data are observed.
Junhyeok Hong, Kipum Kim, Seong W. Kim
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The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models
Proceedings of the AAAI Conference on Artificial IntelligenceThompson sampling (TS) has been known for its outstanding empirical performance supported by theoretical guarantees across various reward models in the classical stochastic multi-armed bandit problems. Nonetheless, its optimality is often restricted to specific priors due to the common observation that TS is fairly insensitive to the choice of the ...
Jongyeong Lee +2 more
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Interpretable Variational Graph Autoencoder with Noninformative Prior [PDF]
Future Internet, 2021 Variational graph autoencoder, which can encode structural information and attribute information in the graph into low-dimensional representations, has become a powerful method for studying graph-structured data. However, most existing methods based on variational (graph) autoencoder assume that the prior of latent variables obeys the standard normal ...
Lili Sun +3 more
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A Noninformative Prior on a Space of Distribution Functions [PDF]
Entropy, 2017 In a given problem, the Bayesian statistical paradigm requires the specification of a prior distribution that quantifies relevant information about the unknowns of main interest external to the data. In cases where little such information is available, the problem under study may possess an invariance under a transformation group that encodes a lack of
Alexander Terenin, David Draper
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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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