Essays In Bayesian Inference And Deep Learning

Go to main content Essays in Bayesian Inference and Deep Learning Xu, Jianeng 2021 Download Formats

Format
BibTeX	View Download
MARCXML	View Download
TextMARC	View Download
MARC	View Download
DataCite	View Download
DublinCore	View Download
EndNote	View Download
NLM	View Download
RefWorks	View Download
RIS	View Download

Add to Basket Cite Citation

Files

Abstract

I have written three essays in the area of Bayesian inference and deep learning. The first essay uses the theory of normal variance-mean mixtures to derive a data augmentation scheme for models that include gamma functions. The second essay introduces and develops a weighted Bayesian bootstrap for machine learning and statistics. The last essay studies the characteristics-sorted factor model in empirical asset pricing and designs a nonreduced-form feedforward neural network with the non-arbitrage objective to minimize pricing errors.

Details

Title Essays in Bayesian Inference and Deep Learning Author Xu, Jianeng : University of Chicago Degree Type Ph.D. Content Type Dissertation Academic Advisor Nicholas Polson Committee Member Ruey Tsay Dacheng Xiu Veronika Rockova Keywords Data Augmentation; Deep learning; Exponential Reciprocal Gamma; Factor Model; Firm Characteristics; Weighted Bootstrap Subjects Statistics Digital Object Identifier https://doi.org/10.6082/uchicago.3392 Publication Date 2021-08 Language English Copyright Statement © 2021 Jianeng Xu Licensing CC BY-ND Record Appears in Booth School of Business > Booth School of Business Dissertations All Record Created 2021-09-29

PDF

Statistics

Downloads Unique Downloads Views Unique Views from to By Days By Months By Years Update Export Download Full History