Results 31 to 40 of about 471,788 (278)
Price adjustment to news with uncertain precision [PDF]
, 2008 Bayesian learning provides the core concept of processing noisy information. In standard Bayesian frameworks, assessing the price impact of information requires perfect knowledge of news’ precision.
Hautsch, Nikolaus +2 more
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Hybrid Optimization Algorithm for Bayesian Network Structure Learning
Information, 2019 Since the beginning of the 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one of the hotspots and important achievements in artificial intelligence research.
Xingping Sun +5 more
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Generalized Bayes Estimation Based on a Joint Type-II Censored Sample from K-Exponential Populations
Mathematics, 2023 Generalized Bayes is a Bayesian study based on a learning rate parameter. This paper considers a generalized Bayes estimation to study the effect of the learning rate parameter on the estimation results based on a joint censored sample of type-II ...
Yahia Abdel-Aty +2 more
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IEEE Transactions on Fuzzy Systems, 2018 In this paper we propose a novel approach for learning from data using rule based fuzzy inference systems where the model parameters are estimated using Bayesian inference and Markov Chain Monte Carlo (MCMC) techniques. We show the applicability of the method for regression and classification tasks using synthetic data-sets and also a real world ...
Indranil Pan, Dirk Bester
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Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing It [PDF]
, 2017 We empirically show that Bayesian inference can be inconsistent under misspecification in simple linear regression problems, both in a model averaging/selection and in a Bayesian ridge regression setting.
Grünwald, Peter, van Ommen, Thijs
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Introduction to Bayesian learning [PDF]
ACM SIGGRAPH 2004 Course Notes, 2004 Sophisticated computer graphics applications require complex models of appearance, motion, natural phenomena, and even artistic style. Such models are often difficult or impossible to design by hand. Recent research demonstrates that, instead, we can "learn" a dynamical and/or appearance model from captured data, and then synthesize realistic new data ...
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Bayesian Learning Without Recall
IEEE Transactions on Signal and Information Processing over Networks, 2017 We analyze a model of learning and belief formation in networks in which agents follow Bayes rule yet they do not recall their history of past observations and cannot reason about how other agents' beliefs are formed. They do so by making rational inferences about their observations which include a sequence of independent and identically distributed ...
Jadbabaie, Ali +2 more
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Variational inference for robust sequential learning of multilayered perceptron neural network [PDF]
FME Transactions, 2015 We derive a new sequential learning algorithm for Multilayered Perceptron (MLP) neural network robust to outliers. Presence of outliers in data results in failure of the model especially if data processing is performed on-line or in real time.
Vuković Najdan +2 more
doaj
Updating for Externalists [PDF]
, 2021 The externalist says that your evidence could fail to tell you what evidence you do or not do have. In that case, it could be rational for you to be uncertain about what your evidence is. This is a kind of uncertainty which orthodox Bayesian epistemology
Gallow, J. Dmitri
core
Designing Visualisations for Bayesian Problems According to Multimedia Principles
Education Sciences, 2022 Questions involving Bayesian Reasoning often arise in events of everyday life, such as assessing the results of a breathalyser test or a medical diagnostic test. Bayesian Reasoning is perceived to be difficult, but visualisations are known to support it.
Theresa Büchter +6 more
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