Results 91 to 100 of about 1,995,230 (269)

Bayesian posterior probabilities: revisited

, 2010
7 pages, including 3 ...
openaire   +2 more sources

CLinNET: An Interpretable and Uncertainty‐Aware Deep Learning Framework for Multi‐Modal Clinical Genomics

Advanced Science, EarlyView.
Identifying disease‐causing genes in neurocognitive disorders remains challenging due to variants of uncertain significance. CLinNET employs dual‐branch neural networks integrating Reactome pathways and Gene Ontology terms to provide pathway‐level interpretability of genomic alterations.
Ivan Bakhshayeshi, Mohammad Mahdi Hosseini, Ahmadreza Argha, Roxana Zahedi, Nigel H. Lovell, Hamid Alinejad‐Rokny   +5 more
wiley   +1 more source

Transient Antiskyrmion‐Mediated Topological Transitions in Isotropic Magnets

Advanced Science, EarlyView.
A transient antiskyrmion‐mediated pathway that drives repeated stripe‐to‐skyrmion transitions is revealed, producing a net increase in topological charge in isotropic Dzyaloshinskii–Moriya interaction films. Experiments and simulations identify the antiskyrmion as a metastable excitation, enabling stochastic bitstream generation for probabilistic ...
Bingqian Dai, Tianyi Wang, Albert Lee, Shijie Xu, Zhongjian Bian, Xinyue Zhu, Puyang Huang, Aadi Chaturvedi, Yaochen Li, Yang Cheng, Qingyuan Shu, Haoran He, Hanshen Huang, Lixuan Tai, Kin Wong, Jinbo Yang, Anjan Soumyanarayanan, Zhaochu Luo, Kang L. Wang   +18 more
wiley   +1 more source

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.
openaire   +5 more sources

Learning a Flexible K-Dependence Bayesian Classifier from the Chain Rule of Joint Probability Distribution

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
doaj   +1 more source

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, Chung, Uijung, Seo, Min-Gwan   +2 more
openaire   +2 more sources

Raman spectroscopy and artificial intelligence to predict the Bayesian probability of breast cancer. [PDF]

Sci Rep, 2021
Kothari R, Jones V, Mena D, Bermúdez Reyes V, Shon Y, Smith JP, Schmolze D, Cha PD, Lai L, Fong Y, Storrie-Lombardi MC.   +10 more
europepmc   +1 more source

The little-hierarchy problem is a little problem: understanding the difference between the big- and little-hierarchy problems with Bayesian probability [PDF]

, 2015
Andrew Fowlie
openalex   +1 more source

A Note of Caution on Maximizing Entropy

Entropy, 2014
The Principle of Maximum Entropy is often used to update probabilities due to evidence instead of performing Bayesian updating using Bayes’ Theorem, and its use often has efficacious results.
Richard E. Neapolitan, Xia Jiang
doaj   +1 more source