Results 131 to 140 of about 3,120,690 (293)
Empirical evaluation of scoring functions for Bayesian network model selection
BMC Bioinformatics, 2012 In this work, we empirically evaluate the capability of various scoring functions of Bayesian networks for recovering true underlying structures. Similar investigations have been carried out before, but they typically relied on approximate learning ...
Liu Zhifa, Malone Brandon, Yuan Changhe
doaj +1 more source
Advanced Science, EarlyView.By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin +12 more
wiley +1 more source
Inference in Bayesian Networks.
AI Mag., 1999 A Bayesian network is a compact, expressive representation of uncertain relationships among parameters in a domain. In this article, I introduce basic methods for computing with Bayesian networks, starting with the simple idea of summing the probabilities of events of interest. The article introduces major current methods for exact computation, briefly
openaire +2 more sources
Generation of Probabilistic Bits by Exploiting Orthogonal Spin Currents in Magnetic Trilayers
Advanced Science, EarlyView.Fe/Ti/CoFeB trilayers generate orthogonal spin currents that drive stochastic spin–orbit‐torque switching for probabilistic‐bit operation. The switching probability is continuously controlled by the in‐plane magnetic field and drive current, enabling tunable random bit generation.
Donghyeon Han +17 more
wiley +1 more source
Model assisted approaches to complex survey sampling from finite populations using Bayesian Networks [PDF]
A class of estimators based on the dependency structure of a multivariate variable of interest and the survey design is defined. The dependency structure is the one described by the Bayesian networks. This class allows ratio type estimators as a subclass
Paola Vicard, Marco Ballin, Mauro Scanu
core
Advanced Science, EarlyView.An interpretable machine learning framework integrating SHAP and PDP analysis identifies critical design descriptors from 139 physicochemical features for Nb─Si alloys. The framework achieves <7% prediction error and guides the discovery of Nb38.5Ti38.5Si3Zr18V2 alloy with 22.791 MPa·m1/2 fracture toughness, breaking the 20 MPa·m1/2 barrier.
Dezhi Chen +7 more
wiley +1 more source
Applying Bayesian networks to model uncertainty in project scheduling [PDF]
, 2009 PhDRisk Management has become an important part of Project Management. In spite of numerous advances in the field of Project Risk Management (PRM), handling uncertainty in complex projects still remains a challenge.
Khodakarami, Vahid
core +2 more sources
Structural learning of bayesian networks using statistical constraints [PDF]
, 2012 Bayesian Networks are probabilistic graphical models that encode in a compact manner the conditional probabilistic relations over a set of random variables.
Venco, Francesco
core
Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics
Advanced Science, EarlyView.Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai +3 more
wiley +1 more source
, 2012 In this article, we consider the problem faced by a sensor network operator who must infer, in real time, the value of some environmental parameter that is being monitored at discrete points in space and time by a sensor network.
Osborne, Michael A. +8 more
core +1 more source