A Bayesian Approach to Multistate Hidden Markov Models: Application to Dementia Progression
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Publication:3304825
DOI10.1080/01621459.2019.1594831zbMath1437.62656arXiv1802.02691OpenAlexW2963064277WikidataQ128182090 ScholiaQ128182090MaRDI QIDQ3304825
R. Jack Clifford Jr., Jonathan P. Williams, Terry M. Therneau, Curtis B. Storlie, Jan Hannig
Publication date: 3 August 2020
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1802.02691
Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Markov processes: estimation; hidden Markov models (62M05)
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Uses Software
Cites Work
- Bayesian analysis of continuous time Markov chains with application to phylogenetic modelling
- Spike and slab variable selection: frequentist and Bayesian strategies
- Markov Chains With Measurement Error: Estimating the `True' Course of a Marker of the Progression of Human Immunodeficiency Virus Disease
- A Bayesian Reliability Analysis of Neutron-Induced Errors in High Performance Computing Hardware
- Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)
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