Modeling the complex dynamics and changing correlations of epileptic events

DOI10.1016/j.artint.2014.05.006zbMath1408.92011arXiv1402.6951OpenAlexW2015875947WikidataQ42686536 ScholiaQ42686536MaRDI QIDQ460608

Emily B. Fox, Drausin F. Wulsin, Brian Litt

Publication date: 13 October 2014

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1402.6951

zbMATH Keywords

time seriesgraphical modelEEGBayesian nonparametricfactorial hidden Markov model

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50) Software, source code, etc. for problems pertaining to biology (92-04)

Related Items (3)

A Bayesian nonparametric model for multi-label learning ⋮ Cooperative hierarchical Dirichlet processes: superposition vs. maximization ⋮ The Convex Mixture Distribution: Granger Causality for Categorical Time Series

Uses Software

• BayesDA

Cites Work

• Unnamed Item
• Unnamed Item
• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• A sticky HDP-HMM with application to speaker diarization
• Multivariate time series modeling and classification via hierarchical VAR mixtures
• Hyper Markov laws in the statistical analysis of decomposable graphical models
• Factorial hidden Markov models
• Hierarchical Dirichlet Processes
• Handbook of Markov Chain Monte Carlo
• Exact and approximate sum representations for the Dirichlet process
• Simulation of hyper-inverse Wishart distributions in graphical models

This page was built for publication: Modeling the complex dynamics and changing correlations of epileptic events