Andrew B. Collier / @datawookie

Reading Bayesian Computation with R by Jim Albert (Springer, 2009) inspired a fit of enthusiasm. Admittedly, I was on a plane coming back from Amsterdam and looking for distractions. I decided to put together a Shiny app to illustrate successive Bayesian updates. I had not yet seen anything that did this to my satisfaction. I like to think that my results come pretty close.

As an aside for a Social Media Automation project I have constructed a bot which uses data from the World Wide Lightning Location Network (WWLLN) to construct daily animated maps of global lightning activity and post them on my Twitter feed. The bot runs remotely and autonomously on an EC2 instance.

Word clouds have become a bit cliché, but I still think that they have a place in giving a high level overview of the content of a corpus. Here are the steps I took in putting together the word cloud for the International Conference on Machine Learning (2015).

Sundry notes from the fourth day of the International Conference for Machine Learning (ICML 2015) in Lille, France. Some of this might not be entirely accurate. Caveat emptor.

Celeste: Variational inference for a generative model of astronomical images (Jeffrey Regier, Andrew Miller, Jon McAuliffe, Ryan Adams, Matt Hoffman, Dustin Lang, David Schlegel, Prabhat)

Colour modelled as a 4 dimensional vector. The Physics (Planck’s Law) places some constraints on the components of these vectors. Light density model accounts for rotation as well as asymmetry of galactic axes.

Selected scribblings from the third day at the International Conference for Machine Learning (ICML 2015) in Lille, France. I’m going out on a limb with some of this, since the more talks I attend, the more acutely aware I become of my limited knowledge of the cutting edge of Machine Learning. Caveat emptor.

Adaptive Belief Propagation (Georgios Papachristoudis, John Fisher)

Belief Propagation describes the passage of messages across a network. The focus of this talk was Belief Propagation within a tree. The authors consider an adaptive algorithm and found that their technique, AdaMP, was significantly better than the current state of the art algorithm, RCTreeBP.

Some notes from the second day at the International Conference for Machine Learning (ICML 2015) in Lille, France. Don’t quote me on any of this because it’s just stuff that I jotted down during the course of the day. Also much of the material discussed in these talks lies outside my field of expertise. Caveat emptor.

Two Big Challenges in Machine Learning (Léon Bottou)

Machine Learning is an Exact Science. It’s also an Experimental Science. It’s also Engineering.

Started the day with a run through the early morning streets of Lille. This city seems to wake up late because it was still nice and quiet well after sunrise. Followed by a valiant attempt to sample everything on the buffet breakfast. I’ll know where to focus my attention tomorrow.

ICML2015

The first day of the International Conference on Machine Learning (ICML 2015) in Lille consisted of tutorials in two parallel streams. Evidently the organisers are not aware of my limited attention span because these tutorials each had nominal durations of longer than 2 hours, punctuated by a break in the middle.

“Machine Learning with R Cookbook” by Chiu Yu-Wei is nothing more or less than it purports to be: a collection of 110 recipes for applying Data Analysis and Machine Learning techniques in R. I was asked by the publishers to review this book and found it to be an interesting and informative read. It will not help you understand how Machine Learning works (that’s not the goal!) but it will help you quickly learn how to apply Machine Learning techniques to you own problems.

I read an interesting article in the 10 June 2015 edition of The Wall Street Journal. The graphic below concisely illustrates the focused strategy being adopted by Disney: a steady decline in the number of movies released per year accompanied by a steady ascent in operating margin.

RStudio does not like Uniform Naming Convention (UNC) paths. This can be a problem if, for example, you install it under Citrix. The solution is to create a suitable environment file.

I recently finished some work on a Shiny application which incorporated a Random Forest model. The model was stored in a .rda file and loaded by server.R during initialisation. This worked fine when tested locally but when I tried to deploy the application on shinyapps.io I ran into a problem: evidently you can only upload server.R and ui.R files. Nothing else.

With only a few days to go until race day, most Comrades Marathon athletes will focusing on resting, getting enough sleep, hydrating, eating and giving a wide berth to anybody who looks even remotely ill.

They will probably also be thinking a lot about Sunday’s race. What will the weather be like? Will it be cold at the start? (Unlikely since it’s been so warm in Durban.) How will they feel on the day? Will they manage to find their seconds along the route?

Faceted plots in ggplot2 are phenomenal. They give you a simple way to break down an array of plots according to the values of one or more categorical variables. But what if you want to stack plots of different variables? Not quite so simple. But certainly possible. I gathered together this solution from a variety of sources on Stack Overflow, notably this one and this other one. A similar issue for vertical alignment is addressed here.

An interesting paper (Modelling the recent common ancestry of all living humans, Nature, 431, 562–566, 2004) by Rohde, Olson and Chang concludes with the words:

With the circlize package, putting together a Chord Diagram is simple.

Predicting Comrades Marathon finishing times is challenging and people have tried a few different approaches. Lindsey Parry, for example, suggests that you use two and a half times your recent marathon time. Sports Digest provides a calculator which predicts finishing time using recent times over three distances. I understand that this calculator is based on the work of Norrie Williamson.