On Sunday the Tokyo Olympics men sprint 100m final will take place. Francesc Montané reminded me in his analysis that 9 years ago I used a simple regression model to predict the winning time for the 100m men sprint final of the 2012 Olympics in London. My model predicted a winning time of 9.68s, yet Usain Bolt finished in 9.63s. For this Sunday my prediction is 9.72s, with a 50% credible interval of [9.

Finally, the Insurance Data Science conference was back last week. After last year’s cancellation due to Covid-19 over 250 delegates from around the world came together on-line for the third instalment of the conference.
The event kicked-off, or should we say lifted off, with a keynote by Thomas Wiecki, CEO of PyMC Labs, on Wednesday. Thomas explained how probabilistic programming can be used to assess risk and make decision in the context of insuring rocket launches.

This article illustrates how ordinary differential equations and multivariate observations can be modelled and fitted with the brms package (Bürkner (2017)) in R1.
As an example I will use the well known Lotka-Volterra model (Lotka (1925), Volterra (1926)) that describes the predator-prey behaviour of lynxes and hares. Bob Carpenter published a detailed tutorial to implement and analyse this model in Stan and so did Richard McElreath in Statistical Rethinking 2nd Edition (McElreath (2020)).

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