11 August 2020 | 12:00 UTC/GMT
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There have been major advances in both the theory and application of hidden Markov models in numerous sub-disciplines of the biosciences. From a theoretical perspective, this work has been in modeling higher-order Markov dependence, extensions to semi-Markov processes, incorporating mixtures of hidden Markov models, and the joint modeling of hidden Markov models and survival data.
From an applied perspective, these theoretical contributions have been motivated from interesting new applications in longitudinal data, cancer and germline genetics, and the natural history of disease processes. I was fortunate to assemble a group of speakers who have made important contributions in these areas over the past few years. Much of the work being presented relates to recent papers in Biometrics, Statistics in Medicine, Biostatistics, and JASA. All talks will present both theory and applications, with a slightly different mix of the two across speakers. The audience of the session will be exposed to the latest methodological/theoretical challenges and new areas where these models have been applied.
Paul S. Albert, National Cancer Institute
Xin Yuan Song, The Chinese University of Hong Kong
Bayesian adaptive group lasso with semiparametric hidden Markov Models
Hyo-young Choo-Wosoba, National Cancer Institute
Hidden Markov modeling approach for identifying Tumor Subclones in cancer
Francesco Bartolucci, The Universita di Perugia
A shared-parameter continuous-time hidden Markov and survival model for longitudinal data with informative droupout
Abhra Sarkar, Department of Statistics and Data Sciences, The University of Texas at Austin
Bayesian nonparametric Modeling of Higher Order Markov Chains