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May 29

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

May 27

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

May 26

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

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