Publications by Rasmus Bååth
A Bayesian Twist on Tukey’s Flogs
In the last post I described flogs, a useful transform on proportions data introduced by John Tukey in his Exploratory Data Analysis. Flogging a proportion (such as, two out of three computers were Macs) consisted of two steps: first we “started” the proportion by adding 1/6 to each of the counts and then we “folded” it using what was bas...
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How Do You Write Your Model Definitions?
I’m often irritated by that when a statistical method is explained, such as linear regression, it is often characterized by how it can be calculated rather than by what model is assumed and fitted. A typical example of this is that linear regression is often described as a method that uses ordinary least squares to calculate the best fitting li...
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Easy Laplace Approximation of Bayesian Models in R
Thank you for tuning in! In this post, a continuation of Three Ways to Run Bayesian Models in R, I will: Handwave an explanation of the Laplace Approximation, a fast and (hopefully not too) dirty method to approximate the posterior of a Bayesian model. Show that it is super easy to do Laplace approximation in R, basically four lines of code. Put...
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Shaping up Laplace Approximation using Importance Sampling
In the last post I showed how to use Laplace approximation to quickly (but dirtily) approximate the posterior distribution of a Bayesian model coded in R. This is just a short follow up where I show how to use importance sampling as an easy method to shape up the Laplace approximation in order to approximate the true posterior much better. But fi...
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An Animation of the t Distribution as a Mixture of Normals
You’ve probably heard about the t distribution. One good use for this distribution is as an alternative to the normal distribution that is more robust against outliers. But where does the t distribution come from? One intuitive characterization of the t is as a mixture of normal distributions. More specifically, as a mixture of an infinite numb...
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Three Syntax Additions that Would Make R Even More Awesome
So R is awesome. Felt good to get that out of the way! But sometimes I long for some small syntax additions while programming away in R in the night… The picture is of a restaurant called Risotto in Berlin. Just to make it clear, I fully understand that changing the syntax of a language as popular as R is a pretty big thing and I’m not argui...
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The Mascots of Bayesian Statistics
Why would Bayesian statistics need a mascot/symbol/logo? Well, why not? I don’t know of any other branch of statistics that has a mascot but many programming languages have. R has an “R”, Python has a python snake, and Go has an adorable little gopher. While Bayesian statistics isn’t a programming language it could be characterized as a t...
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An Animation of the Construction of a Confidence Interval
I’m playing blog ping-pong with John Kruschke’s Doing Bayesian Data Analysis blog as he was partly inspired by my silly post on Bayesian mascots when writing a nice piece on Icons for the essence of Bayesian and frequentist data analysis. That piece, in turn, inspired me resulting in the following wobbly backhand. The confidence interval is,...
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Bayesian First Aid
So I have a secret project. Come closer. I’m developing an R package that implements Bayesian alternatives to the most commonly used statistical tests. Yes you heard me, soon your t.testing days might be over! The package aims at being as easy as possible to pick up and use, especially if you are already used to the classical .test functions. T...
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Bayesian First Aid: Binomial Test
The binomial test is arguably the conceptually simplest of all statistical tests: It has only one parameter and an easy to understand distribution for the data. When introducing null hypothesis significance testing it is puzzling that the binomial test is not the first example of a test but sometimes is introduced long after the t-test and the AN...
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