Bulletin of the Australian Mathematical Society: Volume 111 -

EFFICIENT INFERENCE FOR SPATIAL AND SPATIO-TEMPORAL STATISTICAL MODELS USING BASIS-FUNCTION AND DEEP-LEARNING METHODS
Part of:
- Parametric inference
- Linear inference, regression
MATTHEW SAINSBURY-DALE
Published online by Cambridge University Press:

04 October 2024, pp. 182-183
- Article
- - You have access
  - Open access
- PDF
- HTML
- Export citation
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.

Bulletin of the Australian Mathematical Society