BaNDyT (Bayesian Network analisis of molecular Dynamic simulation Trajectories): software package that implements the Bayesian Network Modeling specifically attuned to the MD simulation trajectories ...
Abstract: This article introduces the Bayesian probabilistic fuzzy neural network (BPFNN), a unified architecture designed to overcome the challenges of conventional fuzzy clustering and neural ...
A representation of the cause-effect mechanism is needed to enable artificial intelligence to represent how the world works. Bayesian Networks (BNs) have proven to be an effective and versatile tool ...
Anomaly response in aerospace systems increasingly relies on multi-model analysis in digital twins to replicate the system’s behaviors and inform decisions. However, computer model calibration methods ...
Traditional statistical and machine learning methods mostly focus on correlations, but causal models allow researchers to infer mechanisms and predict the effects of interventions. Nevertheless, the ...
Implementation of BANSAC, a new guided sampling process for RANSAC. Previous methods either assume no prior information about the inlier/outlier classification of data points or use some previously ...
Imagine a scenario where a team of doctors faces a perplexing medical puzzle. A patient shows a range of symptoms, each pointing to multiple possible diseases. How can they navigate this diagnostic ...
School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, U.K. School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, U.K. The Alan Turing Institute, London NW1 2DB, U.K. School ...
Data from human subjects as well as animals show that working memories are associated with a sense of uncertainty. Indeed, a sense of uncertainty is what allows an observer to properly weigh new ...
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