Accurate estimation of the change in crime over time is a critical first step towards better understanding public safety large urban environments. Bayesian hierarchical modeling natural way to study spatial variation dynamics at neighborhood level, since it facilitates principled sharing information between spatially adjacent neighborhoods. Typically, however, cities contain many physical and s...

Inferring gene regulatory networks from expression data is difficult, but it is common and often useful. Most network problems are under-determined--there are more parameters than data points--and therefore data or parameter set reduction is often necessary. Correlation between variables in the model also contributes to confound network coefficient inference. In this paper, we present an algori...

JU-HYUN PARK: Bayesian Density Regression and Predictor-Dependent Clustering. (Under the direction of Dr. David Dunson.) Mixture models are widely used in many application areas, with finite mixtures of Gaussian distributions applied routinely in clustering and density estimation. With the increasing need for a flexible model for predictor-dependent clustering and conditional density estimation...

This work relates the framework of model-based clustering for spatial functional data where the data are surfaces. We first introduce a Bayesian spatial spline regression model with mixed-effects (BSSR) for modeling spatial function data. The BSSR model is based on Nodal basis functions for spatial regression and accommodates both common mean behavior for the data through a fixed-effects part, ...

We propose a randomized greedy search algorithm to find point estimate for random partition based on loss function and posterior Monte Carlo samples. Given the large size awkward discrete nature of space, minimization expected is challenging. Our approach stochastic series optimizations performed in order embarrassingly parallel. consider several functions, including Binder variation informatio...

In the context of big-data analysis, clustering technique holds significant importance for effective categorization and organization extensive datasets. However, pinpointing ideal number clusters handling high-dimensional data can be challenging. To tackle these issues, several strategies have been suggested, such as a consensus ensemble that yields more outcomes compared to individual models. ...