Computation and Visualisation for large-scale Gaussian updates
In geostatistics, and also in other applications in science and engineering, we are now performing updates on Gaussian process models with many thousands or even millions of components. These large-scale inferences involve computational challenges, because the updating equations cannot be solved as...
Main Authors: | , , |
---|---|
Format: | Report |
Language: | unknown |
Published: |
arXiv
2014
|
Subjects: | |
Online Access: | https://dx.doi.org/10.48550/arxiv.1406.5005 https://arxiv.org/abs/1406.5005 |
id |
ftdatacite:10.48550/arxiv.1406.5005 |
---|---|
record_format |
openpolar |
spelling |
ftdatacite:10.48550/arxiv.1406.5005 2023-05-15T14:02:26+02:00 Computation and Visualisation for large-scale Gaussian updates Rougier, Jonathan Mangion, Andrew Zammit Schoen, Nana 2014 https://dx.doi.org/10.48550/arxiv.1406.5005 https://arxiv.org/abs/1406.5005 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Computation stat.CO FOS Computer and information sciences Preprint Article article CreativeWork 2014 ftdatacite https://doi.org/10.48550/arxiv.1406.5005 2022-04-01T12:49:34Z In geostatistics, and also in other applications in science and engineering, we are now performing updates on Gaussian process models with many thousands or even millions of components. These large-scale inferences involve computational challenges, because the updating equations cannot be solved as written, owing to the size and cost of the matrix operations. They also involve representational challenges, to account for judgements of heterogeneity concerning the underlying fields, and diverse sources of observations. Diagnostics are particularly valuable in this situation. We present a diagnostic and visualisation tool for large-scale Gaussian updates, the `medal plot'. This shows the updated uncertainty for each observation, and also summarises the sharing of information across observations, as a proxy for the sharing of information across the state vector. It allows us to `sanity-check' the code implementing the update, but it can also reveal unexpected features in our modelling. We discuss computational issues for large-scale updates, and we illustrate with an application to assess mass trends in the Antarctic Ice Sheet. Report Antarc* Antarctic Ice Sheet DataCite Metadata Store (German National Library of Science and Technology) Antarctic The Antarctic |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
Computation stat.CO FOS Computer and information sciences |
spellingShingle |
Computation stat.CO FOS Computer and information sciences Rougier, Jonathan Mangion, Andrew Zammit Schoen, Nana Computation and Visualisation for large-scale Gaussian updates |
topic_facet |
Computation stat.CO FOS Computer and information sciences |
description |
In geostatistics, and also in other applications in science and engineering, we are now performing updates on Gaussian process models with many thousands or even millions of components. These large-scale inferences involve computational challenges, because the updating equations cannot be solved as written, owing to the size and cost of the matrix operations. They also involve representational challenges, to account for judgements of heterogeneity concerning the underlying fields, and diverse sources of observations. Diagnostics are particularly valuable in this situation. We present a diagnostic and visualisation tool for large-scale Gaussian updates, the `medal plot'. This shows the updated uncertainty for each observation, and also summarises the sharing of information across observations, as a proxy for the sharing of information across the state vector. It allows us to `sanity-check' the code implementing the update, but it can also reveal unexpected features in our modelling. We discuss computational issues for large-scale updates, and we illustrate with an application to assess mass trends in the Antarctic Ice Sheet. |
format |
Report |
author |
Rougier, Jonathan Mangion, Andrew Zammit Schoen, Nana |
author_facet |
Rougier, Jonathan Mangion, Andrew Zammit Schoen, Nana |
author_sort |
Rougier, Jonathan |
title |
Computation and Visualisation for large-scale Gaussian updates |
title_short |
Computation and Visualisation for large-scale Gaussian updates |
title_full |
Computation and Visualisation for large-scale Gaussian updates |
title_fullStr |
Computation and Visualisation for large-scale Gaussian updates |
title_full_unstemmed |
Computation and Visualisation for large-scale Gaussian updates |
title_sort |
computation and visualisation for large-scale gaussian updates |
publisher |
arXiv |
publishDate |
2014 |
url |
https://dx.doi.org/10.48550/arxiv.1406.5005 https://arxiv.org/abs/1406.5005 |
geographic |
Antarctic The Antarctic |
geographic_facet |
Antarctic The Antarctic |
genre |
Antarc* Antarctic Ice Sheet |
genre_facet |
Antarc* Antarctic Ice Sheet |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.1406.5005 |
_version_ |
1766272720130015232 |