Posts
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Some R packages: armspp and WoodburyMatrix
Over the past year I’ve published two R packages that I’d like to highlight in this blog post.
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Talk at MCM2019, and our rainfall paper published!
Last week I gave a talk about our rainfall work at MCM2019, a fantastic conference on Monte Carlo methods that was held in Sydney, Australia. The conference was awesome and the organisers deserve many kudos for their great work. I came away with lots of great Monte Carlo insights, and many ideas and techniques to try!
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Sampling sparse Gaussian Markov Random Fields in R with the Matrix package
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.
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New R package on Github - climatedata
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.
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Attending NIPS 2016
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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