Results 11 to 20 of about 698,809 (305)
Entropy, 2021 In theoretical biology, we are often interested in random dynamical systems—like the brain—that appear to model their environments. This can be formalized by appealing to the existence of a (possibly non-equilibrium) steady state, whose density preserves
Thomas Parr +4 more
doaj +8 more sources
Causal surgery under a Markov blanket
Behavioral and Brain Sciences, 2022 Bruineberg et al provide compelling clarity on the roles Markov blankets could (and perhaps should) play in the study of life and mind. However, here we draw attention to a further role blankets might play: as a hypothesis about cognition itself. People and other animals may use blanket-like representations to model the boundary between themselves ...
Daniel Yon, Philip Robert Corlett
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, 2020 In this thesis, we address the problem of learning the Markov blanket of a quantity from data in an efficient manner. The discovery of the Markov blanket is useful in the feature subset selection problem and in discovering the structure of a Bayesian network.
Yaramakala, Sandeep, Sandeep Yaramakala
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Efficient Markov Blanket Discovery and Its Application
IEEE Transactions on Cybernetics, 2017 In a Bayesian network (BN), a target node is independent of all other nodes given its Markov blanket (MB), and finding the MB has many applications, including feature selection and BN structure learning. We propose a new MB discovery algorithm, simultaneous MB (STMB), to improve the efficiency of the existing topology-based MB discovery algorithms. The
Qiang Ji, Tian Gao
exaly +5 more sources
Classification using Markov blanket for feature selection
2009 IEEE International Conference on Granular Computing, 2009 Selecting relevant features is in demand when a large data set is of interest in a classification task. It produces a tractable number of features that are sufficient and possibly improve the classification performance. This paper studies a statistical method of Markov blanket induction algorithm for filtering features and then applies a classifier ...
Yifeng Zeng, Jian Luo, Shuyuan Lin
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Direct input variables: rethinking the limits of the extended mind hypothesis within predictive processing [PDF]
Frontiers in PsychologyPredictive Processing theories and the Free Energy Principle (FEP) face a dilemma when addressing the problem raised by the Extended Mind Hypothesis (EMH): on the one hand, few can deny that some objects, such as phones, computers, or other people, have ...
Catalin Teoharie
doaj +2 more sources
Oscillatory dynamics as the coordination layer of the organism: waves, Markov blankets, and the virtual space of cognition [PDF]
Frontiers in NeuroscienceNeural oscillations are not a mechanism that implements cognition. We present a new theoretical framework through a synthesis of relevant literature that has emerged in recent years: metabolic activity in the body, including but not limited to neural ...
Thomas Dylan Daniel
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The Markov blanket trick: On the scope of the free energy principle and active inference. [PDF]
Physics of Life Reviews, 2021 The free energy principle (FEP) has been presented as a unified brain theory, as a general principle for the self-organization of biological systems, and most recently as a principle for a theory of every thing.
Vicente Raja +4 more
semanticscholar +2 more sources
Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm.
Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie, 2022 This study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and
F. Lopes +7 more
semanticscholar +2 more sources
Informative Priors for Markov Blanket Discovery [PDF]
, 2012 We present a novel interpretation of information theoretic feature selection as optimization of a discriminative model. We show that this formulation coincides with a group of mutual information based filter heuristics in the literature, and show how our probabilistic framework gives a well-founded extension for informative priors.
Pocock, Adam
core +9 more sources