Over the past few months I’ve been involved in a fun project with Andrew Zammit Mangion and Noel Cressie at the University of Wollongong. This project involves inference over a large spatial field using a model with a latent space distributed as a multivariate Gaussian with a large and sparse precision matrix (it also involves me learning a lot from Andrew and Noel!). This is my first time working with sparse precision matrices, so I’ve been discovering many new things: what working in precision-space rather than covariance-space means, and how to draw samples from such models even when the number of data points is large. In this post I share a little of what I’ve learned, along with R code. A lot of what follows is derived from the excellent book on this topic by Rue and Held.
I’ve recently put an R package on Github, climatedata, that I’ve put together as part of my PhD work. At present, the idea is to help the package user download climate index data, either directly or as calculated from source data.
This is just a quick note to say I’ll be at NIPS in Barcelona this year presenting a poster on ‘Bayesian mixture models for multivariate time series with an application to Australian rainfall data’ as part of the NIPS Time Series Workshop.
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