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December 4, 2019 @ 4:00 pm - 5:00 pm
Title: Statistical analysis for Markov-dependent data
Presenter: Stephen Berg
Abstract: In the first part of the talk, I will discuss modeling the species distribution of trees in Wisconsin’s historical forests, based on data collected from the Wisconsin Public Land Survey (PLS). Our approach uses a finite mixture model with a Markov random field distribution for the latent labels in order to identify and map forest community subtypes, while accounting for spatial correlation. I will introduce a stochastic approximation maximum likelihood method using Markov chain Monte Carlo (MCMC) for estimating model parameters and discuss the resulting community maps for the PLS data. In the second part of the talk, I will introduce a novel control variate method for increasing the efficiency of MCMC simulations involving deterministic sweep Markov chains. I will demonstrate that the proposed control variate estimator is easy to implement and outperforms the existing methodology both theoretically and empirically for arbitrary two-component Gibbs samplers.