Fabian Dablander PhD Student Methods & Statistics

Visualising the COVID-19 Pandemic

This blog post first appeared on the Science versus Corona blog. It introduces this Shiny app. The novel coronavirus has a firm grip on nearly all countries across the world, and there is large heterogeneity in how countries have responded to the threat. Some countries, such as Brazil and the United States, have fared exceptionally poorly. Oth... Read more

Interactive exploration of COVID-19 exit strategies

The COVID-19 pandemic will end only when a sufficient number of people have become immune, thus preventing future outbreaks. Principally, so-called exit strategies differ on whether immunity is achieved through natural infections, or whether it is achieved through a vaccine. Countries across the world are scrambling to find an adequate exit stra... Read more

Infectious diseases and nonlinear differential equations

Last summer, I wrote about love affairs and linear differential equations. While the topic is cheerful, linear differential equations are severely limited in the types of behaviour they can model. In this blog post, which I spent writing in self-quarantine to prevent further spread of SARS-CoV-2 — take that, cheerfulness — I introduce nonlinear ... Read more

Reviewing one year of blogging

Writing blog posts has been one of the most rewarding experiences for me over the last year. Some posts turned out quite long, others I could keep more concise. Irrespective of length, however, I have managed to publish one post every month, and you can infer the occassional frenzy that ensued from the distribution of the dates the posts appeare... Read more

An introduction to Causal inference

An extended version of this blog post is available from here. Causal inference goes beyond prediction by modeling the outcome of interventions and formalizing counterfactual reasoning. In this blog post, I provide an introduction to the graphical approach to causal inference in the tradition of Sewell Wright, Judea Pearl, and others. We first ... Read more

A brief primer on Variational Inference

Bayesian inference using Markov chain Monte Carlo methods can be notoriously slow. In this blog post, we reframe Bayesian inference as an optimization problem using variational inference, markedly speeding up computation. We derive the variational objective function, implement coordinate ascent mean-field variational inference for a simp... Read more

Harry Potter and the Power of Bayesian Constrained Inference

If you are reading this, you are probably a Ravenclaw. Or a Hufflepuff. Certainly not a Slytherin … but maybe a Gryffindor? In this blog post, we let three subjective Bayesians predict the outcome of ten coin flips. We will derive prior predictions, evaluate their accuracy, and see how fortune favours the bold. We will also discover a neat tric... Read more

Love affairs and linear differential equations

Differential equations are a powerful tool for modeling how systems change over time, but they can be a little hard to get into. Love, on the other hand, is humanity’s perennial topic; some even claim it is all you need. In this blog post — inspired by Strogatz (1988, 2015) — I will introduce linear differential equations as a means to study the... Read more

The Fibonacci sequence and linear algebra

Leonardo Bonacci, better known as Fibonacci, has influenced our lives profoundly. At the beginning of the $13^{th}$ century, he introduced the Hindu-Arabic numeral system to Europe. Instead of the Roman numbers, where I stands for one, V for five, X for ten, and so on, the Hindu-Arabic numeral system uses position to index magnitude. This leads ... Read more

Spurious correlations and random walks

The number of storks and the number of human babies delivered are positively correlated (Matthews, 2000). This is a classic example of a spurious correlation which has a causal explanation: a third variable, say economic development, is likely to cause both an increase in storks and an increase in the number of human babies, hence the correlatio... Read more

Bayesian modeling using Stan: A case study

Practice makes better. And faster. But what exactly is the relation between practice and reaction time? In this blog post, we will focus on two contenders: the power law and exponential function. We will implement these models in Stan and extend them to account for learning plateaus and the fact that, with increased practice, not only th... Read more

Two perspectives on regularization

Regularization is the process of adding information to an estimation problem so as to avoid extreme estimates. Put differently, it safeguards against foolishness. Both Bayesian and frequentist methods can incorporate prior information which leads to regularized estimates, but they do so in different ways. In this blog post, I illustrate these tw... Read more

Variable selection using Gibbs sampling

“Which variables are important?” is a key question in science and statistics. In this blog post, I focus on linear models and discuss a Bayesian solution to this problem using spike-and-slab priors and the Gibbs sampler, a computational method to sample from a joint distribution using only conditional distributions. Variable selection is a beas... Read more

Two properties of the Gaussian distribution

In a previous blog post, we looked at the history of least squares, how Gauss justified it using the Gaussian distribution, and how Laplace justified the Gaussian distribution using the central limit theorem. The Gaussian distribution has a number of special properties which distinguish it from other distributions and which make it easy to wor... Read more

Curve fitting and the Gaussian distribution

Judea Pearl said that much of machine learning is just curve fitting1 — but it is quite impressive how far you can get with that, isn’t it? In this blog post, we will look at the mother of all curve fitting problems: fitting a straight line to a number of points. In doing so, we will engage in some statistical detective work and discover the met... Read more

In Review: Ten Great Ideas About Chance

The blog post reviews and summarizes the book “Ten Great Ideas about Chance” by Diaconis and Skyrms. A much shorter version of this review has been published in Significance, see here. In ten short chapters, Persi Diaconis and Brian Skyrms provide a bird’s eye perspective on probability theory and its connection to other disciplines. The book... Read more