Results 41 to 50 of about 347,983 (230)
Entropy, 2016 In this work, we propose a new approach of deriving the bounds between entropy and error from a joint distribution through an optimization means. The specific case study is given on binary classifications.
Bao-Gang Hu, Hong-Jie Xing
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, 2019 I propose a normative updating rule, extended Bayesianism, for the incorporation of probabilistic information arising from the process of becoming more aware. Extended Bayesianism generalizes standard Bayesian updating to allow the posterior to reside on richer probability space than the prior.
arxiv +1 more source
What is the Probability you are a Bayesian? [PDF]
Journal of Statistics Education, 2014 Bayesian methodology continues to be widely used in statistical applications. As a result, it is increasingly important to introduce students to Bayesian thinking at early stages in their mathematics and statistics education. While many students in upper level probability courses can recite the differences in the Frequentist and Bayesian inferential ...
Timothy J. Robinson, Shaun S. Wulff
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Measurement: Sensors, 2022 The process of revising the Guide to the Expression of Uncertainty in Measurement (GUM) is ongoing. A successful revision must be theoretically sound, so it must be based on a recognized paradigm for scientific data analysis.
R. Willink
doaj
Semiparametric Bayesian Networks [PDF]
arXiv, 2021 We introduce semiparametric Bayesian networks that combine parametric and nonparametric conditional probability distributions. Their aim is to incorporate the advantages of both components: the bounded complexity of parametric models and the flexibility of nonparametric ones.
arxiv
Predicting Facial Biotypes Using Continuous Bayesian Network Classifiers
Complexity, 2018 Bayesian networks are useful machine learning techniques that are able to combine quantitative modeling, through probability theory, with qualitative modeling, through graph theory for visualization.
Gonzalo A. Ruz, Pamela Araya-Díaz
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BMC Medical Research Methodology, 2010 Background Automatic variable selection methods are usually discouraged in medical research although we believe they might be valuable for studies where subject matter knowledge is limited.
Steineck Gunnar +3 more
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Some Applications of Bayes' Rule in Probability Theory to Electrocatalytic Reaction Engineering
International Journal of Electrochemistry, 2011 Bayesian methods stem from the principle of linking prior probability and conditional probability (likelihood) to posterior probability via Bayes' rule.
Thomas Z. Fahidy
doaj +1 more source
A Bayesian Density Model Based Radio Signal Fingerprinting Positioning Method for Enhanced Usability
Sensors, 2018 Indoor navigation and location-based services increasingly show promising marketing prospects. Indoor positioning based on Wi-Fi radio signal has been studied for more than a decade because Wi-Fi, a signal of opportunity without extra cost, is ...
Zheng Li +6 more
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Neural representation of probabilities for Bayesian inference [PDF]
Journal of Computational Neuroscience, 2015 Bayesian models are often successful in describing perception and behavior, but the neural representation of probabilities remains in question. There are several distinct proposals for the neural representation of probabilities, but they have not been directly compared in an example system.
Jose L. Pena +4 more
openaire +3 more sources