Results 101 to 110 of about 2,253,559 (287)
Statistical Science, 2011 Don Fraser has given an interesting account of the agreements and disagreements between Bayesian posterior probabilities and confidence levels. In this comment I discuss some cases where the lack of such agreement is extreme. I then discuss a few cases where it is possible to have Bayes procedures with frequentist validity. Such frequentist-Bayesian—or
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Ensemble Inference and Inferability of Gene Regulatory Networks
PLoS ONE, 2014 The inference of gene regulatory network (GRN) from gene expression data is an unsolved problem of great importance. This inference has been stated, though not proven, to be underdetermined implying that there could be many equivalent (indistinguishable) solutions. Motivated by this fundamental limitation, we have developed new framework and algorithm,
Ud-Dean Minhaz, Gunawan Rudiyanto
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The Archaeological Excavation Report of Rigny: An Example of an Interoperable Logicist Publication
Heritage, 2019 The logicist program, which was initiated in the 1970s by J.C. Gardin, aims to clarify the reasoning processes in the field of archaeology and to explore new forms of publication, in order to overcome the growing imbalance between the flood of ...
Olivier Marlet +4 more
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Perseverers, recencies and deferrers : new experimental evidence for multiple inference strategies in understanding [PDF]
, 1983 In the course of understanding a text, a succession of decision points arise at which readers are faced with the task of choosing among alternative possible interpretations ofthattext.
Granger, Richard H. +1 more
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Combination Strategies for Semantic Role Labeling
, 2011 This paper introduces and analyzes a battery of inference models for the problem of semantic role labeling: one based on constraint satisfaction, and several strategies that model the inference as a meta-learning problem using discriminative classifiers.
Carreras, X. +3 more
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arXiv, 2013 It is suggested that an AI inference system should reflect an inference policy that is tailored to the domain of problems to which it is applied -- and furthermore that an inference policy need not conform to any general theory of rational inference or induction.
arxiv
Probabilistic Alternatives to Bayesianism: The Case of Explanationism
Frontiers in Psychology, 2015 There has been a probabilistic turn in contemporary cognitive science. Far and away, most of the work in this vein is Bayesian, at least in name. Coinciding with this development, philosophers have increasingly promoted Bayesianism as the best normative ...
Igor eDouven, Jonah N. Schupbach
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The Theory and Algorithm of Ergodic Inference [PDF]
arXiv, 2018 Approximate inference algorithm is one of the fundamental research fields in machine learning. The two dominant theoretical inference frameworks in machine learning are variational inference (VI) and Markov chain Monte Carlo (MCMC). However, because of the fundamental limitation in the theory, it is very challenging to improve existing VI and MCMC ...
arxiv
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms [PDF]
arXiv, 2017 Approximate probabilistic inference algorithms are central to many fields. Examples include sequential Monte Carlo inference in robotics, variational inference in machine learning, and Markov chain Monte Carlo inference in statistics. A key problem faced by practitioners is measuring the accuracy of an approximate inference algorithm on a specific data
arxiv
Automatic Variational Inference in Stan [PDF]
, 2015 Variational inference is a scalable technique for approximate Bayesian inference. Deriving variational inference algorithms requires tedious model-specific calculations; this makes it difficult to automate.
Blei, David M. +3 more
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