All Events
Advancement: Nonparametric Bayesian Modeling for Spatial Point Processes
Chunyi Zhao
Ph.D. Student
Abstract: Spatial point processes are stochastic models for spatial point patterns that record the locations of a certain event in a bounded observation window. Our research aims to explore flexible and computationally efficient Bayesian nonparametric prior models for non-homogeneous Poisson processes... Read More
Defense: Inference and Uncertainty Quantification for High-Dimensional Tensor Regression with Tensor Decompositions and Bayesian Methods
Daniel Spencer
PhD Candidate
Abstract: Certain image analysis settings rely on the use of large datasets with an inherent multidimensional data structures known as tensors. We explore different Bayesian modeling techniques for inference on tensor-valued coefficients with sparse, contiguous nonzero regions. We rely on the... Read More
Defense: Inference and Uncertainty Quantification for High-Dimensional Tensor Regression with Tensor Decompositions and Bayesian Methods
Daniel Spencer
Ph.D. Candidate
Abstract: Certain image analysis settings rely on the use of large datasets with an inherent multidimensional data structures known as tensors. We explore different Bayesian modeling techniques for inference on tensor-valued coefficients with sparse, contiguous nonzero regions. We rely on the... Read More
