Stochastic nets and Bayesian regularisation from constraints

Joshua Bon

Research output: ThesisMaster's Thesis

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Abstract

Regularisation in Bayesian modelling uses classes of priors which encourage shrinkage on posterior quantities. This research proposes a new probabilistic interpretation for regularisation by way of constraints. We augment a given probability distribution with a stochastic constraint that restricts the support of the prior, emitting a "regularised" distribution as a result. This introduces the notion of probabilistic regularisation as an operator that acts on a probability distribution, rather than classes of priors which are considered to have desirable shrinkage properties. Several aspects of the so-called stochastic constraint priors are explored, including full Bayesian computation and theoretical results.
Original languageEnglish
QualificationMasters
Awarding Institution
  • The University of Western Australia
Thesis sponsors
Award date20 May 2019
DOIs
Publication statusUnpublished - 2019

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