Recently mathematician Terence Tao posted some ruminations on how to visualize the different values a random variable could take. He created some basic animated loops that cycled through some samples from the distribution, and proposed a way to represent conditionality as well.
I liked the idea so much I’ve added it to my probability distributions JS library. The numbers can be shown directly, or interpreted as waiting times where each arrival is shown with a flashing symbol.
For documentation and examples see http://statisticsblog.com/probability-distributions/#visualize
If you use this feature do me a favor and let me know.
I happened to be travelling through Brussels, so I stopped by Ghent, the world hotspot for research into imprecise probabilities, and setup an interview with Gert de Cooman. Gert has been working in imprecise probabilities for more than twenty years, is a founding member and former President of SIPTA, the Society for Imprecise Probability: Theories and Applications, and has helped organize many of the ISIPTA conferences and SIPTA Schools.
Topics include fair betting rates, Dutch books, Monte Carlo methods, Markov chains, utility, and the foundations of probability theory. We had a rich, wide-ranging discussion. You may need to listen two (or more!) times to process everything.
Episode on SoundCloud
I found a handful of JS libraries that support sampling from distributions, but nothing that lets me use the R syntax I know and (mostly) love. Even more importantly, I would have to trust the quality of the sampling functions, or carefully read through each one and tweak as needed. So I decided to create my own JS library that:
- Conforms to R function names and parameters – e.g.
rnorm(50, 0, 1)
- Uses the best entropy available to simulate randomness
- Includes some non-standard distributions that I’ve been using (more on this below)
I’ve made this library public at Github and npm.
Not a JS developer? Just want to play with the library? I’ve setup a test page here.
Please keep in mind that this library is still in its infancy. I’d highly recommend you do your own testing on the output of any distribution you use. And of course let me know if you notice any issues.
In terms of additional distributions, these are marked “experimental” in the source code. They include the unreliable friend and its discrete cousin the FML, a frighteningly thick-tailed distribution I’ve been using to model processes that may never terminate.
I’m looking to put together a small crew to take on a large arbitrage project. The (rough) model for this would be “Hong Kong Syndicate” which took on the horse betting market. To be involved you have to be willing to make a large commitment in terms of time or money (I plan to contribute both). I have a set of proposed guidelines for identifying potential arbitrage targets, send me an email for more info.
UPDATE (Oct 5): Lots of interest in this, hope to finalize a core team this week. Let me know right away if you’re interested. Also, this would not necessarily be targeted at horse racing.
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