Results 251 to 260 of about 212,322 (298)

Neural posterior estimation for population genetics. [PDF]

Genetics
Min J, Ning Y, Pope NS, Baumdicker F, Kern AD.   +4 more
europepmc   +1 more source

Socio-environmental and health-related factors and their association with longitudinal change in brain neuroimaging markers through the plasma metabolome among UK adults: An additive Bayesian network analysis. [PDF]

Brain Behav Immun Health
Beydoun MA, Weiss J, Gerogescu MF, Ashe J, Maino Vieytes CA, Huang T, Beydoun HA, Noren Hooten N, Turney IC, Evans MK, Zonderman AB.   +10 more
europepmc   +1 more source

A Generalizable and Interpretable Framework for Molecular Subtype Classification of Pancreatic Ductal Adenocarcinoma Integrating Conformal Uncertainty Quantification and Consensus-Based Explainable Artificial Intelligence Across Multiple Cohorts. [PDF]

Int J Mol Sci
Yasar S, Yagin FH, Alzakari SA, Alkhalifa AK, Al-Hashem F, Tabnjh AK.   +5 more
europepmc   +1 more source

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Variance of Bayes estimates

IEEE Transactions on Information Theory, 1971
This paper contains an analysis of the performance of Bayes conditional-mean parameter estimators. The main result is that on a finite parameter space such estimates exhibit a mean-square error that diminishes exponentially with the number of observations, the observations being assumed to be independent.
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Bayes empirical Bayes estimation of a poisson mean

Statistics & Probability Letters, 1985
In the empirical Bayes (EB) decision problem consisting of squared error estimation of a Poisson mean, a prior distribution \(\Lambda\) is placed on the gamma family of prior distributions to produce Bayes EB estimators which are admissible. A subclass of such estimators is shown to be asymptotically optimal (a.o.).
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Some Comments on “Bayes” Estimators

The American Statistician, 1973
(1973). Some Comments on “Bayes” Estimators. The American Statistician: Vol. 27, No. 1, pp. 12-14.
G. C. Tiao, G. E. P. Box
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Sensitivity of bayes and empirical bayes estimates

Communications in Statistics - Theory and Methods, 1990
The main objective is to investigate the behaviour of Bayes and empirical Bayes confidence intervals for a mean to changes from normality in the specification of either the sampling or prior distributions. To do this the posterior mean and variance are calculated when the prior and sampling distributions are defined by Edgeworth expansions, corrective ...
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Bayes Estimates of Haplotype Effects

Genetic Epidemiology, 2001
We describe a Markov chain Monte Carlo implementation of a Bayesian approach to estimating associations of a trait with a large set of haplotypes recently introduced by Clayton and Jones [Am J Hum Genet 65:1161–9, 2000]. The model uses the length of the longest segment in common between any two haplotypes to define the prior correlation structure for ...
D C, Thomas, J L, Morrison, D G, Clayton
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ESTIMATING A BINOMIAL PARAMETER: IS ROBUST BAYES REAL BAYES?

Statistics & Risk Modeling, 1993
Summary: In robust Bayesian analysis, a prior is assumed to belong to a family instead of being specified exactly. The multiplicity of priors naturally leads to a collection of Bayes actions (estimates), and these often form a convex set (an interval in the case of a real parameter).
Zen, Mei-Mei, DasGupta, A.
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