Modelling, inference and optimization in probabilistic machine learning

Thesis

Bayesian machine learning has gained tremendous attention in the machine learning community over the past few years. Bayesian methods offer a coherent reasoning for quantifying uncertainties in the decision making procedure, based on the Bayes rule. One of the core advantages of Bayesian methods is the separation of modelling and inference. In other words, the likelihood models are completely independent of the computation of the posterior distribution of the parameters.

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Authors: Lu, X

• DOI: 10.5287/ora-qod6godmo
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English