Publications by Rasmus Bååth
Chillin’ at UseR! 2014
This year’s UseR! conference was held at the University of California in Los Angeles. Despite the great weather and a nearby beach, most of the conference was spent in front of projector screens in 18° c (64° f) rooms because there were so many interesting presentations and tutorials going on. I was lucky to present my R package Bayesian Firs...
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drinkR: Estimate your Blood Alcohol Concentration using R and Shiny.
Inspired by events that took place at UseR 2014 last month I decided to implement an app that estimates one’s blood alcohol concentration (BAC). Today I present to you drinkR, implemented using R and Shiny, Rstudio’s framework for building web apps using R. So, say that I had a good dinner, drinking a couple of glasses of wine, followed by an...
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Bayesian First Aid: Poisson Test
As the normal distribution is sort of the default choice when modeling continuous data (but not necessarily the best choice), the Poisson distribution is the default when modeling counts of events. Indeed, when all you know is the number of events during a certain period it is hard to think of any other distribution, whether you are modeling the ...
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Tiny Data, Approximate Bayesian Computation and the Socks of Karl Broman
Big data is all the rage, but sometimes you don’t have big data. Sometimes you don’t even have average size data. Sometimes you only have eleven unique socks: Karl Broman is here putting forward a very interesting problem. Interesting, not only because it involves socks, but because it involves what I would like to call Tiny Data™. The pro...
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Probable Points and Credible Intervals, Part 1
After having broken the Bayesian eggs and prepared your model in your statistical kitchen the main dish is the posterior. The posterior is the posterior is the posterior, given the model and the data it contains all the information you need and anything else will be a little bit less nourishing. However, taking in the posterior in one gulp can be...
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Tidbits from the Books that Defined S (and R)
Why R? Because S! R is the open source implementation (and a pun!) of S, a language for statistical computing that was developed at Bell Labs in the late 1970s. After that, the implementation of S underwent a number of major revisions documented in a series of seminal books, often just referred to by the color of their cover: The Brown Book, the ...
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How to Summarize a 2D Posterior Using a Highest Density Ellipse
Making a slight digression from last month’s Probable Points and Credible Intervals here is how to summarize a 2D posterior density using a highest density ellipse. This is a straight forward extension of the highest density interval to the situation where you have a two-dimensional posterior (say, represented as a two column matrix of samples)...
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Eight Christmas Gift Ideas for the Statistically Interested
Christmas is soon upon us and here are some gift ideas for your statistically inclined friends (or perhaps for you to put on your own wish list). If you have other suggestions please leave a comment! 🙂 1. Games of probability A recently released game where probability takes the main role is Pairs, an easy going press-your-luck game that can be...
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Peter Norvig’s Spell Checker in Two Lines of Base R
Peter Norvig, the director of research at Google, wrote a nice essay on How to Write a Spelling Corrector a couple of years ago. That essay explains and implements a simple but effective spelling correction function in just 21 lines of Python. Highly recommended reading! I was wondering how many lines it would take to write something similar in b...
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Probable Points and Credible Intervals, Part 2: Decision Theory
“Behind every great point estimate stands a minimized loss function.” – Me, just now This is a continuation of Probable Points and Credible Intervals, a series of posts on Bayesian point and interval estimates. In Part 1 we looked at these estimates as graphical summaries, useful when it’s difficult to plot the whole posterior in good wa...
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