Results 111 to 120 of about 3,284,626 (292)
Object-Oriented Bayesian Networks for a Decision Support System [PDF]
We study an economic decision problem where the actors are two rms and the Antitrust Authority whose main task is to monitor and prevent rms potential anti-competitive behaviour.
Paola Vicard +2 more
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Efficient utility-based clustering over high dimensional partition spaces [PDF]
, 2009 Because of the huge number of partitions of even a moderately sized dataset, even when Bayes factors have a closed form, in model-based clustering a comprehensive search for the highest scoring (MAP) partition is usually impossible.
Smith, JQ +9 more
core +1 more source
Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials
Advanced Science, EarlyView.Traditional black‐box models for polymer mechanics rely solely on data and lack physical interpretability. This work presents a physics‐embedded neural network (PENN) that integrates constitutive equations into machine learning. The approach ensures reliable stress predictions, provides interpretable parameters, and enables performance‐driven, inverse ...
Siqi Zhan +8 more
wiley +1 more source
Developing a Bayesian Network Model for Environmental Risks of the Caspian Sea Breakwater in Bandar Anzali [PDF]
علوم محیطیIntroduction :Managing environmental risks associated with marine installations, such as the breakwaters of the Caspian Sea, plays a critical role in mitigating potential hazards and ensuring sustainable development.
Hamid Sarkheil +2 more
doaj +1 more source
Transformers are Bayesian Networks
CoRRTransformers are the dominant architecture in AI, yet why they work remains poorly understood. This paper offers a precise answer: a transformer is a Bayesian network. We establish this in five ways. First, we prove that every sigmoid transformer with any weights implements weighted loopy belief propagation on its implicit factor graph.
openaire +3 more sources
Improved learning of Bayesian networks
CoRR, 2001 The search space of Bayesian Network structures is usually defined as Acyclic Directed Graphs (DAGs) and the search is done by local transformations of DAGs. But the space of Bayesian Networks is ordered by DAG Markov model inclusion and it is natural to consider that a good search policy should take this into account.
Kocka, T., Castelo Valdueza, R.
openaire +4 more sources
ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals
Advanced Science, EarlyView.The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray +3 more
wiley +1 more source
Bayesian Neural Networks via MCMC: A Python-Based Tutorial
IEEE AccessBayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain Monte-Carlo (MCMC) sampling methods are used to implement Bayesian ...
Rohitash Chandra, Joshua Simmons
doaj +1 more source
Bayesian networks for fMRI: A primer
NeuroImage, 2014 Bayesian network analysis is an attractive approach for studying the functional integration of brain networks, as it includes both the locations of connections between regions of the brain (functional connectivity) and more importantly the direction of the causal relationship between the regions (directed functional connectivity).
Jeanette A. Mumford, Joseph D. Ramsey
openaire +2 more sources
Accounting for multiple impacts of the Common agricultural policies in rural areas: an analysis using a Bayesian networks approach [PDF]
In evaluating the potential effects of the reforms of the Common Agricultural Policy, a particularly challenging issue is the representation of the complexity of rural systems either in a static or dynamic framework.
Viaggi, Davide +2 more
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