Results 241 to 250 of about 20,022 (282)

Explainable artificial intelligence for stroke risk stratification in atrial fibrillation. [PDF]

Eur Heart J Digit Health
Zimmerman RM, Hernandez EJ, Tristani-Firouzi M, Yandell M, Steinberg BA.   +4 more
europepmc   +1 more source

Time series-based forecasting of infectious disease outbreak using information systems in public health. [PDF]

Front Public Health
Du M.
europepmc   +1 more source

Bayesian networks for predicting clinical outcomes in COVID-19 patients: A retrospective study in a resource-limited setting. [PDF]

PLoS One
Filamant TC, Raherinirina AF, Totohasina A.   +2 more
europepmc   +1 more source

Probabilistic Graphical Models for Computational Biomedicine

Methods of Information in Medicine, 2003
Summary Background: As genomics becomes increasingly relevant to medicine, medical informatics and bioinformatics are gradually converging into a larger field that we call computational biomedicine. Objectives: Developing a computational framework that is common to the different disciplines that compose computational biomedicine ...
Bart De Moor
exaly   +4 more sources

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Probabilistic Graphical Models

Advances in Computer Vision and Pattern Recognition, 2021
The most important problem in machine learning is to estimate and infer the value of unknown variables (e.g., class label) based on the observed evidence (e.g., training samples). Probabilistic models provide a framework that considers learning problems as computing the probability distributions of variables.
L Enrique Sucar
exaly   +3 more sources

Preconditioner Approximations for Probabilistic Graphical Models

, 2018
We present a family of approximation techniques for probabilistic graphical models, based on the use of graphical preconditioners developed in the scientific computing literature. Our framework yields rigorous upper and lower bounds on event probabilities and the log partition function of undirected graphical models, using non-iterative procedures that
Pradeep Ravikumar, John D. Lafferty
openaire   +3 more sources

Probabilistic Graphical Models and Their Inferences (Tutorial)

2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W), 2019
Probabilistic graphical models are useful for modelling stochastic phenomena for doing inferences and reasoning under uncertainty. Especially, chain graph models and Bayesian networks can be used as probabilistic expert systems where inferences can be done with junction tree algorithm, etc.
Wijayatunga, Priyantha,
openaire   +2 more sources