Simon, NoahMatsen, FrederickFisher, Thayer2023-08-142023-08-142023-08-142023Fisher_washington_0250E_25368.pdfhttp://hdl.handle.net/1773/50247Thesis (Ph.D.)--University of Washington, 2023In this dissertation, we will discuss three applications for neural networks in the paradigm of Bayesian estimation and inference. In Chapter 2, we describe a likelihood-free method of estimating posterior quantiles using recurrent neural networks. This method is particularly useful for time series data with high-dimensional latent random variables. In Chapter 3, we propose a method for optimizing Bayesian adaptive enrichment design clinical trials using reinforcement learning. Through the simulation of many trials, we use policy gradient descent to train a neural network to optimally incorporate existing patient information into a simple adaptive enrollment criterion. Finally, in Chapter 4, we describe a mechanistically explicit forward model for Somatic Hypermutation (SHM). We estimate the parameters regulating this model with a neural network and manually selected summary statistics.application/pdfen-USnoneBayesianDeep learningReinforcement learningBiostatisticsBiostatisticsThesis