Results 311 to 320 of about 1,895,431 (338)
Comprehensive Protein Inference Analysis with PyProteinInference Elucidates Biological Understanding of Tandem Mass Spectrometry Data. [PDF]
J Proteome ResHinkle TB, Bakalarski CE.
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Design and experimental research of on device style transfer models for mobile environments. [PDF]
Sci RepHwang I, Oh T.
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N-methyl-d-aspartate receptor hypofunction causes recurrent and transient failures of perceptual inference. [PDF]
BrainWeilnhammer V +8 more
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Tsbrowse: an interactive browser for Ancestral Recombination Graphs
Karthikeyan S +3 more
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IMA Journal of Management Mathematics, 1996
Abstract Reject inference has an established role in the development of scorecards for credit applications. The performance of the rejects, had they been accepted, is inferred to be good or bad in order to obtain a complete picture of the population applying for credit.
G. Platts, G. Bennett, J. Crossley
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Abstract Reject inference has an established role in the development of scorecards for credit applications. The performance of the rejects, had they been accepted, is inferred to be good or bad in order to obtain a complete picture of the population applying for credit.
G. Platts, G. Bennett, J. Crossley
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MATHEMATICAL INFERENCE AND LOGICAL INFERENCE
The Review of Symbolic Logic, 2018AbstractThe deviation ofmathematical proof—proof in mathematical practice—from the ideal offormal proof—proof in formal logic—has led many philosophers of mathematics to reconsider the commonly accepted view according to which the notion of formal proof provides an accurate descriptive account of mathematical proof.
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2012
This chapter provides an overview of the Bayesian approach to data analysis, modeling, and statistical decision making. The topics covered go from basic concepts and definitions (random variables, Bayes' rule, prior distributions) to various models of general use in biology (hierarchical models, in particular) and ways to calibrate and use them (MCMC ...
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This chapter provides an overview of the Bayesian approach to data analysis, modeling, and statistical decision making. The topics covered go from basic concepts and definitions (random variables, Bayes' rule, prior distributions) to various models of general use in biology (hierarchical models, in particular) and ways to calibrate and use them (MCMC ...
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2009
Chapter 9:This contribution concerns statistical inference for parametric models used in stochastic geometry and based on quick and simple simulation free procedures as well as more comprehensive methods based on a maximum likelihood or Bayesian approach combined with markov chain Monte Carlo (MCMC) techniques.
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Chapter 9:This contribution concerns statistical inference for parametric models used in stochastic geometry and based on quick and simple simulation free procedures as well as more comprehensive methods based on a maximum likelihood or Bayesian approach combined with markov chain Monte Carlo (MCMC) techniques.
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Trends in Cognitive Sciences, 2012
Recent developments in decision-making research are bringing the topic of planning back to center stage in cognitive science. This renewed interest reopens an old, but still unanswered question: how exactly does planning happen? What are the underlying information processing operations and how are they implemented in the brain?
Marc Toussaint, Matthew Botvinick
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Recent developments in decision-making research are bringing the topic of planning back to center stage in cognitive science. This renewed interest reopens an old, but still unanswered question: how exactly does planning happen? What are the underlying information processing operations and how are they implemented in the brain?
Marc Toussaint, Matthew Botvinick
openaire +3 more sources