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Title: Bayesian modeling of multivariate non Gaussian time series Authors:  Refik Soyer - George Washington University (United States) [presenting]
Abstract: Modeling of multivariate non Gaussian time series of correlated observations is considered. In so doing, we focus on time series from multivariate counts and durations. Dependence among series arises as a result of sharing a common dynamic environment. We discuss characteristics of the resulting multivariate time series models and develop Bayesian inference for them using particle filtering and Markov chain Monte Carlo methods.