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Zen and the Art of Making a Bayesian Espresso

In this issue of Neuron, Konovalov and Krajbich (2018) argue that a Bayesian inference is employed when learning new sequences and identify distinct brain networks that track the uncertainty of both the current state and the underlying pattern structure. In this issue of Neuron, Konovalov and Krajbi... Full description

Journal Title: Neuron (Cambridge Mass.), 2018-06-27, Vol.98 (6), p.1066-1068
Main Author: Zhang, Lei
Other Authors: Redžepović, Saša , Rose, Michael , Gläscher, Jan
Format: Electronic Article Electronic Article
Language: English
Subjects:
Quelle: Alma/SFX Local Collection
Publisher: United States: Elsevier Inc
ID: ISSN: 0896-6273
Link: https://www.ncbi.nlm.nih.gov/pubmed/29953869
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recordid: cdi_proquest_miscellaneous_2062832388
title: Zen and the Art of Making a Bayesian Espresso
format: Article
creator:
  • Zhang, Lei
  • Redžepović, Saša
  • Rose, Michael
  • Gläscher, Jan
subjects:
  • Bayes Theorem
  • Bayesian analysis
  • Brain
  • Coffee
  • Learning
  • Medical imaging
  • Neurosciences
  • Uncertainty
ispartof: Neuron (Cambridge, Mass.), 2018-06-27, Vol.98 (6), p.1066-1068
description: In this issue of Neuron, Konovalov and Krajbich (2018) argue that a Bayesian inference is employed when learning new sequences and identify distinct brain networks that track the uncertainty of both the current state and the underlying pattern structure. In this issue of Neuron, Konovalov and Krajbich (2018) argue that a Bayesian inference is employed when learning new sequences and identify distinct brain networks that track the uncertainty of both the current state and the underlying pattern structure.
language: eng
source: Alma/SFX Local Collection
identifier: ISSN: 0896-6273
fulltext: fulltext
issn:
  • 0896-6273
  • 1097-4199
url: Link


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descriptionIn this issue of Neuron, Konovalov and Krajbich (2018) argue that a Bayesian inference is employed when learning new sequences and identify distinct brain networks that track the uncertainty of both the current state and the underlying pattern structure. In this issue of Neuron, Konovalov and Krajbich (2018) argue that a Bayesian inference is employed when learning new sequences and identify distinct brain networks that track the uncertainty of both the current state and the underlying pattern structure.
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abstractIn this issue of Neuron, Konovalov and Krajbich (2018) argue that a Bayesian inference is employed when learning new sequences and identify distinct brain networks that track the uncertainty of both the current state and the underlying pattern structure. In this issue of Neuron, Konovalov and Krajbich (2018) argue that a Bayesian inference is employed when learning new sequences and identify distinct brain networks that track the uncertainty of both the current state and the underlying pattern structure.
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