Now showing items 1-6 of 6
Bayesian Nonparametric Probabilistic Methods in Machine Learning
Many aspects of modern science, business and engineering have become data-centric, relying on tools from Artificial Intelligence and Machine Learning. Practitioners and researchers in these fields need tools that can ...
Fault Diagnosis and Prognosis in Industrial Systems Using Machine Learning Techniques
This dissertation concerns the development and usage of advanced machine learning and signal processing methods for fault diagnosis and prognosis in industrial systems. It establishes a mathematical framework for ...
Three Essays on Migration, Occupational Sorting, and Degree Choice: Analyses of Spatial Autocorrelation, Income and the Race Wage Gap
This dissertation consists of three essays concerning migration, occupational sorting, and college major choice. They each examine income, Chapter 1 concerning aggregate income at the county level, while Chapters 2 and ...
Three Essays for the Retail Planner: Spatializing Bass Temporalizing Huff and Visualizing the Ensemble
This dissertation integrates and extends an ensemble of classic spatial and temporal models seeking to contribute to retail trade area theory. The manuscript represents an integrated approach to location intelligence across ...
Three Papers Addressing Migration Induced Autocorrelation in Spatial Analysis Within a Bayesian Modelling Framework
Migration within a regional system influences demographic, epidemiological, and socioeconomic processes. At its core is the spatial redistribution of population and their associated characteristics. This redistribution ...
A Bayesian Hierarchical Framework for Pathway Analysis in Genome-Wide Association Studies
The genome-wide association studies (GWAS) aim to identify genetic variants, typically single nucleotide polymorphisms (SNPs), associated with a disease/trait. A commonly used analytic strategy in GWAS is to test for ...