Results 31 to 40 of about 147,580 (306)
Bayesian statistical inference
Statistica, 2017 This work was translated into English and published in the volume: Bruno De Finetti, Induction and Probability, Biblioteca di Statistica, eds. P. Monari, D.
Bruno De Finetti
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Bayesian Inference with Projected Densities
SIAM/ASA Journal on Uncertainty Quantification, 2023 Constraints are a natural choice for prior information in Bayesian inference. In various applications, the parameters of interest lie on the boundary of the constraint set. In this paper, we use a method that implicitly defines a constrained prior such that the posterior assigns positive probability to the boundary of the constraint set.
Jasper M. Everink +2 more
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Deep bootstrap for Bayesian inference
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2023 For a Bayesian, the task to define the likelihood can be as perplexing as the task to define the prior. We focus on situations when the parameter of interest has been emancipated from the likelihood and is linked to data directly through a loss function.
Lizhen Nie, Veronika Ročková
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Bayesian parameter inference and model selection by population annealing in systems biology. [PDF]
PLoS ONE, 2014 Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection.
Yohei Murakami
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BMC Evolutionary Biology, 2005 Background Bayesian phylogenetic inference holds promise as an alternative to maximum likelihood, particularly for large molecular-sequence data sets.
Harlow Timothy J +2 more
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Journal of Statistical Software, 2022 Consider a Bayesian inference problem where a variable of interest does not take values in a Euclidean space. These "non-standard" data structures are in reality fairly common. They are frequently used in problems involving latent discrete factor models,
Alexandre Bouchard-Côté +7 more
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Confirmation in the Cognitive Sciences: The Problematic Case of Bayesian Models [PDF]
, 2011 Bayesian models of human learning are becoming increasingly popular in cognitive science. We argue that their purported confirmation largely relies on a methodology that depends on premises that are inconsistent with the claim that people are Bayesian ...
Danks, David +3 more
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Granger causality vs. dynamic Bayesian network inference: a comparative study [PDF]
, 2009 Background In computational biology, one often faces the problem of deriving the causal relationship among different elements such as genes, proteins, metabolites, neurons and so on, based upon multi-dimensional temporal data.
Denby Katherine J +8 more
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Fuzzy Bayesian Inference [PDF]
, 2009 Data are frequently not precise numbers but more or less non-precise, also called fuzzy. Moreover a-priori information in Bayesian inference is usually not available as a precise probability distribution. In case of fuzzy data and fuzzy a-priori information Bayes' theorem has to be generalized.
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Generalized Bayesian Inference Nets Model and Diagnosis of Cardiovascular Diseases
Journal of Intelligent Systems, 2011 A generalized Bayesian inference nets model (GBINM) is proposed to aid researchers to construct Bayesian inference nets for various applications. The benefit of such a model is well demonstrated by applying GBINM in constructing a hierarchical Bayesian ...
Sekar Booma Devi +2 more
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