Show simple item record

dc.contributor.authorDuffield, Samuel
dc.description.abstractThe common theme of this thesis is the concept of using Monte Carlo techniques to approximate a sequence of probability distributions. Novel methodological contributions are found in Chapter 3 through to Chapter 6. In Chapter 3 we derive a method for the complete characterisation of online statistical models where Monte Carlo approximations are defined sequentially as new data arrive. We then demonstrate the utility of this method in Chapter 4 for the compelling application of de-noising sequential GPS coordinates to be restricted to a road network. In Chapter 5 and Chapter 6, the sequence of probability distributions are defined artificially in order to gradually (and more effectively) approach a single offline target probability distribution. Chapter 5 adopts ideas from high-dimensional time series to efficiently tackle the difficult setting where we cannot evaluate the density of the target distribution and instead can only generate synthetic data. Chapter 6 explores the use of a scalable Hessian approximation in the more common scenario where the target density can be evaluated and even differentiated. Finally, Chapter 7 describes a general purpose software package that can be used to implement and customise all of the discussed algorithms at competitive speeds.
dc.rightsAttribution-NonCommercial 4.0 International (CC BY-NC 4.0)
dc.subjectBayesian inference
dc.subjectMonte Carlo
dc.subjectState-space Models
dc.titleIterative Monte Carlo Approximations for Bayesian Inference
dc.type.qualificationnameDoctor of Philosophy (PhD)
dc.publisher.institutionUniversity of Cambridge
dc.publisher.collegeSt Edmunds
pubs.funder-project-idEPSRC (1890282)
pubs.funder-project-idEngineering and Physical Sciences Research Council (1890282)
cam.supervisorSingh, Sumeetpal
datacite.issupplementedby.doiPredicting Taxi Passenger Demand Using Streaming Data
datacite.issupplementedby.doiPhilatelic Mixtures and Multimodal Densities
pubs.licence-display-nameApollo Repository Deposit Licence Agreement

Files in this item


This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Except where otherwise noted, this item's licence is described as Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)