Bayesian non-homogeneous hidden Markov model with variable selection for investigating drivers of seizure risk cycling
Author:
Affiliation:
1. Department of Statistics, Rice University
2. Department of Neurology, University of California, San Francisco
3. Department of Neurology, Baylor College of Medicine
4. Seizure Tracker, LLC
Publisher
Institute of Mathematical Statistics
Subject
Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability
Reference43 articles.
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2. SCOTT, S. L. (2002). Bayesian methods for hidden Markov models: Recursive computing in the 21st century. J. Amer. Statist. Assoc. 97 337–351.
3. ARTHURS, S., ZAVERI, H. P., FREI, M. G. and OSORIO, I. (2010). Patient and caregiver perspectives on seizure prediction. Epilepsy Behav. 19 474–477.
4. AYUB, N., CHIANG, S., MOSS, R. and GOLDENHOLZ, D. (2020). Natural history of generalized motor seizures: A retrospective analysis. Seizure 80 109–112.
5. BAUD, M. O., KLEEN, J. K., MIRRO, E. A., ANDRECHAK, J. C., KING-STEPHENS, D., CHANG, E. F. and RAO, V. R. (2018). Multi-day rhythms modulate seizure risk in epilepsy. Nat. Commun. 9 88.
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