Articles by Fabian Dablander

Estimating the risks of partying during a pandemic

July 22, 2020 | Fabian Dablander

There is no doubt that, every now and then, one ought to celebrate life. This usually involves people coming together, talking, laughing, dancing, singing, shouting; simply put, it means throwing a party. With temperatures rising, summer offers all the more incentive to organize such a joyous event. Blinded by the ...
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Visualising the COVID-19 Pandemic

June 19, 2020 | Fabian Dablander

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 thre... [Read more...]

Infectious diseases and nonlinear differential equations

March 22, 2020 | Fabian Dablander

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 ...
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Reviewing one year of blogging

December 27, 2019 | Fabian Dablander

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 ...
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An introduction to Causal inference

November 30, 2019 | Fabian Dablander

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 rehash the common adage that correlation is not ...
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A brief primer on Variational Inference

October 30, 2019 | Fabian Dablander

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 simple linear regression example in ...
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Love affairs and linear differential equations

August 29, 2019 | Fabian Dablander

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 ...
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The Fibonacci sequence and linear algebra

July 28, 2019 | Fabian Dablander

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 ...
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Spurious correlations and random walks

June 29, 2019 | Fabian Dablander

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 ...
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Bayesian modeling using Stan: A case study

May 30, 2019 | Fabian Dablander

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 ...
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Two perspectives on regularization

April 15, 2019 | Fabian Dablander

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 ...
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Variable selection using Gibbs sampling

March 31, 2019 | Fabian Dablander

“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 ...
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