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EFFICIENT INFERENCE FOR SPATIAL AND SPATIO-TEMPORAL STATISTICAL MODELS USING BASIS-FUNCTION AND DEEP-LEARNING METHODS
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EFFICIENT INFERENCE FOR SPATIAL AND SPATIO-TEMPORAL STATISTICAL MODELS USING BASIS-FUNCTION AND DEEP-LEARNING METHODS
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Journal Article
EFFICIENT INFERENCE FOR SPATIAL AND SPATIO-TEMPORAL STATISTICAL MODELS USING BASIS-FUNCTION AND DEEP-LEARNING METHODS
2025
Overview
Inference in spatial and spatio-temporal models can be challenging for a variety of reasons. For example, non-Gaussianity often leads to analytically intractable integrals; we may be in a ‘big’ data setting, whereby the number of observations renders traditional methods too computationally expensive; we may wish to make inferences over spatial supports that are different to those of our measurements; or, we may wish to use a statistical model whose likelihood function is either unavailable or computationally intractable. In this thesis, I develop several techniques that help to alleviate these challenges.
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