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Big Data ideas for GLMs

The goal of this project is to develop new Bayesian methods for large-scale data analysis using subsampling techniques. The focus of the project will be on generalised linear models (GLMs), which are commonly used models in statistics and machine learning.One of the main challenges in using Bayesian statistics with big data is the high computational cost associated with processing big datasets. The proposed project aims to address this challenge by developing new subsampling techniques for Piecewise Deterministic Markov Process (PDMP) …

Study level
Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Data Science

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