# Scalable Spatio-Temporal Smoothing via Hierarchical Sparse Cholesky Decomposition

arXiv: https://arxiv.org/abs/2207.09384 · HTML: https://mlsanity.com/paper/2207.09384/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2022.

## Built on by (most cited first, 1 of 1)

- [Adaptive LASSO estimation for functional hidden dynamic geostatistical model](https://mlsanity.com/paper/2208.05528/) (2022)

## Abstract (arXiv)

We propose an approximation to the forward-filter-backward-sampler (FFBS) algorithm for large-scale spatio-temporal smoothing. FFBS is commonly used in Bayesian statistics when working with linear Gaussian state-space models, but it requires inverting covariance matrices which have the size of the latent state vector. The computational burden associated with this operation effectively prohibits its applications in high-dimensional settings. We propose a scalable spatio-temporal FFBS approach based on the hierarchical Vecchia approximation of Gaussian processes, which has been previously successfully used in spatial statistics. On simulated and real data, our approach outperformed a low-rank FFBS approximation.