Variational inference for max-stable processes

TL;DR

This paper introduces a variational inference method for max-stable processes, enabling efficient full likelihood inference for high-dimensional spatial extreme value data, surpassing previous computational limitations.

Contribution

It proposes a novel variational inference approach that approximates the likelihood by sampling from a parametric partition distribution, allowing scalable inference for complex max-stable models.

Findings

Enables full likelihood inference in higher dimensions than previous methods

Applicable to large datasets with many observations

Can be extended to a Bayesian framework

Abstract

Max-stable processes provide natural models for the modelling of spatial extreme values observed at a set of spatial sites. Full likelihood inference for max-stable data is, however, complicated by the form of the likelihood function as it contains a sum over all partitions of sites. As such, the number of terms to sum over grows rapidly with the number of sites and quickly becomes prohibitively burdensome to compute. We propose a variational inference approach to full likelihood inference that circumvents the problematic sum. To achieve this, we first posit a parametric family of partition distributions from which partitions can be sampled. Second, we optimise the parameters of the family in conjunction with the max-stable model to find the partition distribution best supported by the data, and to estimate the max-stable model parameters. In a simulation study we show that our method enables full likelihood inference in higher dimensions than previous methods, and is readily applicable to data sets with a large number of observations. Furthermore, our method can easily be extended to a Bayesian setting. Code is available at https://github.com/LPAndersson/MaxStableVI.jl.