Bayesian methods in epidemiology has advanced rapidly in the past two decades. Yet a lot need to be done to accommodate new challenges arising from expanded and new epidemiology data. This PI initiated project focuses on building modeling machinery for time to pregnancy data and for data on measurement agreement and diagnostic statistics. Moreover, the project also investigates new Bayesian computational algorithm for handling massive epidemiology data using sequential Markov chain Monte Carlo method or variational approach.

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