Results 41 to 50 of about 377,664 (139)
Bayesian Nonlinear Support Vector Machines for Big Data
, 2017 We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the proposed method is faster than competing Bayesian approaches and scales ...
Deutsch, Matthaeus +3 more
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From Bayesian Inference to Logical Bayesian Inference [PDF]
, 2018 Bayesian Inference (BI) uses the Bayes’ posterior whereas Logical Bayesian Inference (LBI) uses the truth function or membership function as the inference tool. LBI is proposed because BI is not compatible with the classical Bayes’ prediction and does not use logical probability and hence cannot express semantic meaning. In LBI, statistical probability
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Network Plasticity as Bayesian Inference
PLOS Computational Biology, 2015 33 pages, 5 figures, the supplement is available on the author's web page http://www.igi.tugraz.at ...
Stefan Habenschuss +3 more
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Bayesian quantile regression: An application to the wage distribution in 1990s Britain [PDF]
This paper illustrates application of Bayesian inference to quantile regression. Bayesian inference regards unknown parameters as random variables, and we describe an MCMC algorithm to estimate the posterior densities of quantile regression parameters ...
Van Kerm, Philippe +2 more
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A Bayesian Reflection on Surfaces
, 1998 The topic of this paper is a novel Bayesian continuous-basis field representation and inference framework. Within this paper several problems are solved: The maximally informative inference of continuous-basis fields, that is where the basis for the ...
RG Brown, S Geman
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A Generalization of Bayesian Inference [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1968 Summary Procedures of statistical inference are described which generalize Bayesian inference in specific ways. Probability is used in such a way that in general only bounds may be placed on the probabilities of given events, and probability systems of this kind are suggested both for sample information and for prior information.
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Fuzzy Bayesian inference [PDF]
1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, 2002 Bayesian methods provide formalism for reasoning about partial beliefs under conditions of uncertainty. Given a set of exhaustive and mutually exclusive hypotheses, one can compute the probability of a hypothesis for a given evidence using the Bayesian inversion formula.
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Stochastic Variational Inference
, 2013 We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. We develop this technique for a large class of probabilistic models and we demonstrate it with two probabilistic topic models, latent Dirichlet ...
Chong Wang +4 more
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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. Cocchi, Clueb, Bologna, 1993. Bayesian statistical Inference is one of the last fundamental philosophical papers in which we can find the essential De Finetti's approach to the statistical ...
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Symbolic Exact Inference for Discrete Probabilistic Programs
, 2019 The computational burden of probabilistic inference remains a hurdle for applying probabilistic programming languages to practical problems of interest.
Broeck, Guy Van den +2 more
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