Results 61 to 70 of about 7,255,480 (346)
Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee [PDF]
Pesquisa Agropecuária Brasileira, 2017 : The objective of this work was to evaluate the use of artificial neural networks in comparison with Bayesian generalized linear regression to predict leaf rust resistance in Arabica coffee (Coffea arabica).
Gabi Nunes Silva +9 more
doaj +2 more sources
, 2020 Provides users with its associated functions for pedagogical purposes in visually learning Bayesian networks and Markov chain Monte Carlo (MCMC) computations.
Aisdl
semanticscholar +1 more source
Loop amplitudes from precision networks
SciPost Physics Core, 2023 Evaluating loop amplitudes is a time-consuming part of LHC event generation. For di-photon production with jets we show that simple, Bayesian networks can learn such amplitudes and model their uncertainties reliably.
Simon Badger, Anja Butter, Michel Luchmann, Sebastian Pitz, Tilman Plehn
doaj +1 more source
Bayesian generalized network design [PDF]
Theoretical Computer Science, 2020 25 pages, 0 figure. An extended abstract of this paper is to appear in the 27th Annual European Symposium on Algorithms (ESA 2019)
Yuval Emek +3 more
openaire +6 more sources
Avoiding spurious feedback loops in the reconstruction of gene regulatory networks with dynamic bayesian networks [PDF]
, 2009 Feedback loops and recurrent structures are essential to the regulation and stable control of complex biological systems. The application of dynamic as opposed to static Bayesian networks is promising in that, in principle, these feedback loops can be ...
Husmeier, D. +3 more
core +1 more source
Searching multiregression dynamic models of resting-state fMRI networks using integer programming [PDF]
, 2015 A Multiregression Dynamic Model (MDM) is a class of multivariate time series that represents various dynamic causal processes in a graphical way. One of the advantages of this class is that, in contrast to many other Dynamic Bayesian Networks, the ...
Smith, Jim +9 more
core +1 more source
Efficient Sampling and Structure Learning of Bayesian Networks [PDF]
Journal of Computational And Graphical Statistics, 2018 Bayesian networks are probabilistic graphical models widely employed to understand dependencies in high-dimensional data, and even to facilitate causal discovery.
J. Kuipers, Polina Suter, G. Moffa
semanticscholar +1 more source
GENERATIONS IN BAYESIAN NETWORKS
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 2019 This paper focuses on the study of some aspects of the theory of oriented graphs in Bayesian networks. In some papers on the theory of Bayesian networks, the concept of “Generation of vertices” denotes a certain set of vertices with many parents ...
Alexander Litvinenko +3 more
doaj +1 more source
Holistic Approach Promotes Failure Prevention of Smart Mining Machines Based on Bayesian Networks
Machines, 2023 In the forthcoming era of fully autonomous mining, spanning from drilling operations to port logistics, novel approaches will be essential to pre-empt hazardous situations in the absence of human intervention. The progression towards complete autonomy in
Madeleine Martinsen +3 more
doaj +1 more source
Bayesian optimization on networks
Journal of Computational PhysicsThis paper studies optimization on networks modeled as metric graphs. Motivated by applications where the objective function is expensive to evaluate or only available as a black box, we develop Bayesian optimization algorithms that sequentially update a Gaussian process surrogate model of the objective to guide the acquisition of query points.
W. Li, D. Sanz-Alonso, R. Yang
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