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Advances in Software and Spatio-Temporal Modelling with Gaussian Processes



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This thesis concerns the use of Gaussian processes (GPs) as distributions over unknown functions in Machine Learning and probabilistic modeling. GPs have been found to have utility in a wide range of applications owing to their flexibility, interpretability, and tractability. I advance their use in three directions.

Firstly, the abstractions upon which software is built for their use in practice. In modern GP software libraries such as GPML, GPy, GPflow, and GPyTorch, the kernel is undoubtedly the dominant abstraction. While it remains highly successful it of course has limitations, and I propose to address some of these through a complementary abstraction: affine transformations of GPs. Specifically I show how a collection of GPs, and affine transformations thereof, can themselves be treated as a single GP. This in turn leads to a design for software, including exact and approximate inference algorithms. I demonstrate the utility of this software through a collection of worked examples, focussing on models which are more cleanly and easily expressed using this new software.

Secondly, I develop a new scalable approximate inference algorithm for a class of GPs commonly utilised in spatio-temporal problems. This is a setting in which GPs excel, for example enabling the incorporation of important inductive biases, and observations made at arbitrary points in time and space. However, the computation required to perform exact inference and learning in GPs scales cubically in the number of observations, necessitating approximation, to which end I combine two important complementary classes of approximation: pseudo-point and Markovian. The key contribution is the insight that a simple and useful way to combine them turns out to be well-justified. This resolves an open question in the literature, provides new insight into existing work, and a new family of approximations. The efficacy of an important member of this family is demonstrated empirically.

Finally I develop a GP model and associated approximate inference techniques for the prediction of sea surface temperatures (SSTs) on decadal time scales, which are relevant when taking planning decisions which consider resilience to climate change. There remains a large degree of uncertainty as to the state of the climate on such time scales, but it is thought to be possible to reduce this by exploiting the predictability of natural variability in the climate. The developed GP-based model incorporates a key assumption used by the existing statistical models employed for decadal prediction, thus retaining a valuable inductive bias, while offering several advantages. Amongst these is the lack of need for spatial aggregation of data, which is especially relevant when data are sparse, as is the case with historical ocean SST data.

In summary, this thesis contributes to the practical use of GPs through a set of abstractions that are useful in the design of software, algorithms for approximate inference in spatial-temporal settings, and their use in decadal climate prediction.





Turner, Richard E


bayesian inference, gaussian process


Doctor of Philosophy (PhD)

Awarding Institution

University of Cambridge