De la prime à la cotisation: retrouver la solidarité
Bon, commençons par un constat que tout le monde peut faire. L’assurance a mauvaise image, mauvaise presse. On la soupçonne d’être une bureaucratie froide, une industrie de paperasse, un partenaire qui cherche l’échappatoire au moment où l’on en a besoin. On parle de primes comme on parlerait d’un prix, et on finit par juger l’assurance comme on juge un achat. Ai-je “rentabilisé” mon contrat cette année ? Ai-je “perdu” de l’argent si je n’ai pas eu de sinistre. Ai-je été un bon client si … Continue reading <span …
From Premium to Contribution: Recovering Solidarity
This post was originally written and published in French, De la prime à la cotisation: retrouver la solidarité Well, let’s start with something everyone can observe. Insurance has a bad image, a bad press. It is often suspected of being a cold bureaucracy, a paperwork industry, a partner that looks for loopholes precisely when you need it. We talk about premiums the way we talk about a price, and we end up judging insurance the way we judge a purchase. Did I “get my money’s … Continue reading <span …
The anti-Bayesian is standing at the back window with a shotgun, scanning for priors coming over the hill, while a million assumptions just walk right into his house through the front door. (also, an interesting point by Yann LeCun in 2012 about human language)
How to Predict Sports in R: Elo, Monte Carlo, and Real Simulations
R • Sports Analytics • Ratings • Monte Carlo • Forecasting Sports are noisy. Teams change. Injuries happen. Upsets happen. But uncertainty is not the enemy—it’s the input. In this hands-on guide you’ll build a practical sports prediction workflow in R using tidyverse, PlayerRatings, and NFLSimulatoR, then connect ratings to Monte …
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