Results 81 to 90 of about 622,033 (280)
Uniqueness of the Level Two Bayesian Network Representing a Probability Distribution
International Journal of Mathematics and Mathematical Sciences, 2011 Bayesian Networks are graphic probabilistic models through which we can acquire, capitalize on, and exploit knowledge. they are becoming an important tool for research and applications in artificial intelligence and many other fields in the last decade ...
Linda Smail
doaj +1 more source
Price Probabilities: A Class of Bayesian and Non-Bayesian Prediction Rules [PDF]
SSRN Electronic Journal, 2016 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Advanced Science, EarlyView.Skeleton‐oriented object segmentation (SKOOTS) introduces a new strategy for 3D mitochondrial instance segmentation by predicting explicit skeletons rather than relying on boundary cues. This approach enables robust analysis of densely packed organelles in large FIB‐SEM datasets.
Christopher J. Buswinka +3 more
wiley +1 more source
Spintronic Bayesian Hardware Driven by Stochastic Magnetic Domain Wall Dynamics
Advanced Science, EarlyView.Magnetic Probabilistic Computing (MPC) utilizes intrinsic stochastic dynamics in domain walls to establish a hardware foundation for uncertainty‐aware artificial intelligence. Thermally driven domain‐wall fluctuations, voltage‐controlled magnetic anisotropy, and TMR readout enable fully electrical, tunable probabilistic inference.
Tianyi Wang +11 more
wiley +1 more source
Entropy, 2015 As one of the most common types of graphical models, the Bayesian classifier has become an extremely popular approach to dealing with uncertainty and complexity.
Limin Wang, Haoyu Zhao
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Comparison Between Bayesian and Frequentist Tail Probability Estimates
, 2019 In this paper, we investigate the reasons that the Bayesian estimator of the tail probability is always higher than the frequentist estimator. Sufficient conditions for this phenomenon are established both by using Jensen's Inequality and by looking at ...
González, Bárbara +2 more
core
SpatialESD: Spatial Ensemble Domain Detection in Spatial Transcriptomics
Advanced Science, EarlyView.ABSTRACT Spatial transcriptomics (ST) measures gene expression while preserving spatial context within tissues. One of the key tasks in ST analysis is spatial domain detection, which remains challenging due to the complex structure of ST data and the varying performance of individual clustering methods. To address this, we propose SpatialESD, a Spatial
Hongyan Cao +11 more
wiley +1 more source
Subjective confidence reflects representation of Bayesian probability in cortex. [PDF]
Nat Hum Behav, 2022 Geurts LS +3 more
europepmc +1 more source
Perfect Regular Equilibrium [PDF]
We propose a revised version of the perfect Bayesian equilibrium in general multi-period games with observed actions. In finite games, perfect Bayesian equilibria are weakly consistent and subgame perfect Nash equilibria.
hanjoon michael, jung/j
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Being Bayesian about Categorical Probability
, 2020 Neural networks utilize the softmax as a building block in classification tasks, which contains an overconfidence problem and lacks an uncertainty representation ability. As a Bayesian alternative to the softmax, we consider a random variable of a categorical probability over class labels. In this framework, the prior distribution explicitly models the
Joo, Taejong +2 more
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