M1 Artificial Intelligence · semester 7 · R

Applied Probability and Statistics

Four practical sessions in R, from vectorised code and first random draws to forecasting temperatures over France by kriging.

Four labs in R

Lab0 A first hour with R Object classes, vectorised code in place of loops, and first random draws with sample() and runif(). Lab1 Real random variables Distribution functions, densities and medians, worked by hand and then checked by simulation and the inversion method. Lab2 Estimators in the exponential model The law of large numbers and the central limit theorem shown by simulation, then method-of-moments and likelihood estimators. Lab3 Kriging temperatures over France Simulating a Gaussian vector from a square root of its covariance, then forecasting by conditioning — kriging, from vectors to a map.

Reading it

Source The folder on GitHub Four lab folders, each with its completed notebook once the work is done. README The course README The full table of labs, what each is solved with, and what is provided going in.

The lab subjects, the official solution and the datasets are not redistributed here. Each lab keeps them on disk, out of the repository; what is committed is the work written against them.