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Data Science resources

Inspired by many similar lists, for example this one by Carl Anderson and this one by Yu Wu, below is a list of free, online resources for learning data science (i.e. programming, machine learning, and statistics). This list includes textbooks (published online or with a free PDF) as well as YouTube videos and online courses (MOOCs). As always with these things, your mileage may vary.

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Canberra talk and other news

I’ll be giving a talk at ANU on Tuesday, for the Canberra branch of the Statistical Society of Australia: “Statistics from Mars – Bayesian signal processing for Raman Spectroscopy.” I’ll also be presenting a talk at the 12th International Conference on Monte Carlo Methods and Applications (MCM 2019) at UTS. I’ve volunteered as the newsletter editor for the Bayes Section of SSA. Our June newsletter is now available online.

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New version 0.4-0 of serrsBayes on CRAN

I’ve just released a new version of my R package serrsBayes. It includes a fix for a major bug, where I was using the upper-triangular instead of the lower-triangular Cholesky factor for random-walk Metropolis proposals. It also includes a reimplementation of fitVoigtPeaksSMC using RcppEigen with OpenMP for parallelism. This function is still slower than fitSpectraSMC, but should result in a big speed improvement if you have OpenMP multi-threading enabled. See the release notes and CRANberries for details.

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End-of-year talks

A couple of seminars that I wanted to highlight in the next couple of weeks: one from A/Prof Mirko Draca (Department of Economics, University of Warwick) and another from me. Mirko will be speaking in the NIASRA Seminar Series this week at Wollongong, while I’ll be flying home to Brisbane next week to present a talk at the ACEMS Workshop on Intractable Likelihoods & ABC. Abstracts for both talks are below:

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Video of my talk in Oaxaca

I was very lucky to be invited to attend a 5 day workshop at the Casa Matemática Oaxaca (CMO-BIRS), “Computational Statistics and Molecular Simulation: A Practical Cross-Fertilization” where I presented my work on the Rao-Blackwellized particle filter for Bayesian modelling of Raman spectroscopy. A link to the video is here and the abstract for my talk is below. See also commentary on selected talks by one of the organisers, Prof. Xi’an.

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Mean and variance of the Potts model in the hottest state

I’ve just arXiv’d another revision of my paper on the PFAB algorithm: arXiv:1503.08066v3 [stat.CO]. It includes a rather elegant proof of the exact mean and variance of the sufficient statistic S(y) in the hottest state, when the inverse temperature \beta = 0. The proof by my co-author Geoff Nicholls holds for any Potts model with first-order neighbours. That is, the nearest 4 neighbours in a 2D lattice (or 6 neighbours in 3D). For posterity, I present my rather clunkier proof below, which involves induction on dimension for a rectangular lattice.

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useR! 2018

It was great to be back in Brissie for the first time since my PhD graduation, 3 years ago. The R Consortium have made video of all of the talks available on YouTube – a link to mine is below, along with my slides.

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Ella Kaye on Ella Kaye

Computational Bayesian statistics

Bayes' Food Cake

A bit of statistics, a bit of cakes. - Blogs to Learn R from the Community

Computational Bayesian statistics

Richard Everitt's blog

Computational Bayesian statistics

Let's Look at the Figures

David Firth's blog

Nicholas Tierney

Computational Bayesian statistics

Sweet Tea, Science

Two southern scientistas will be bringing you all that is awesome in STEM as we complete our PhDs. Ecology, statistics, sass.

Mad (Data) Scientist

Musings, useful code etc. on R and data science

Another Astrostatistics Blog

The random musings of a reformed astronomer ...

Darren Wilkinson's research blog

Statistics, computing, data science, Bayes, stochastic modelling, systems biology and bioinformatics

(badness 10000)

Computational Bayesian statistics

Igor Kromin

Computational Bayesian statistics


I can't get no

Xi'an's Og

an attempt at bloggin, nothing more...

Sam Clifford

Postdoctoral Fellow, Bayesian Statistics, Aerosol Science