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In this phase, participants were taught to search for stimuli hidden by a "mask" (blank comparison), an important repertoire for the performance in the posterior learning tests.
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The sampler can overcome situations where no maximum-likelihood estimator exists, and it can adaptively learn the posterior distribution of highly correlated fitness landscapes without prior knowledge of their shape.
Perceptual features of the landmarks, their visual salience and size, were associated with tonic responses throughout learning in posterior visual areas (factors 3 and 4).
The book shows only its tip — its "posterior," as readers learn in an introductory chapter "What Is a Shell?" Among other things, the introductory material describes how a mollusk forms its shell and how one can read a mollusk's "autobiography" in the bumps and lumps that form there.
In the future, we plan to extend the LANS approach to dynamic networks that evolve in time and to a probabilistic framework in which we learn posterior distributions over edges belonging to a backbone network.
Chen implemented the program for the posterior p-value computation, for learning amino acid functional classes, and for learning the rate matrices associated to each class, and did some of the data analysis.
In the first case, the combined model is a weighted sum of individual posterior probabilities, the weights being new parameters that can be learned from the data.
Though many supervised machine learning algorithms have the ability to coerce classification distribution, the NB machine learning algorithm naturally produces posterior probabilities without coercion [19].
In this study, TES to the posterior parietal cortices improved artificial number learning but impaired automaticity on the learning task, whereas TES to the dorsolateral prefrontal cortices impaired the learning but improved automaticity of artificial number learning (Iuculano and Cohen Kadosh, 2013).
Our results show differential alterations in learning capacity in children with posterior fossa tumours.
We see that the Braun model, while certainly biased towards independence, appears to be learning about ψ1 and has posterior means of approximately 0.65 and 0.80 when ψ1 is equal to 0.70 and 0.90, respectively.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com