Scalable and causal Bayesian inference
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This thesis will focus on two facets of Bayesian estimation. First, we propose methods that can improve parameter estimation in particle filtering when making use of a distributed computing environment by allowing for periodic communication between compute nodes. The periodic communication can improve the embarrassingly parallel version of our particle filter without dramatically increasing the computational costs. Our method is intended for use on data with large N or in streaming settings where latent parameters are changing over time. Secondly, we propose a method for estimating heterogeneous treatment effects in observational studies using transformed response variables via a modification to Bayesian additive regression trees that incorporates a mixture model in the regression error terms.