Francesco-Zeno-Costanzo / IATBDP

repository per codici per il corso di introduzione alla teoria bayesiana della probabilità

IATBDP

Code repository for the Introduction to Bayesian Probability Theory course

Written together with Carlo Panu

Brief explanation of the codes

fit_pol_ran.py

Code that tries to fit the data contained in data.txt with a polynomial model looking for the optimal values ​​of the likelihood calculated in a set of parameters uniformly extracted in a range chosen by the user.

lupi.py

Exercise: Having seen M wolves, having tagged them and recognizing r tagged wolves out of n observed at a later time, estimate the total population N.

metropolis_hastings.py

Sampling a Gaussian using the metropolis-hastings algorithm

SIRD.py

Simulation of SIRD model

nested_sampling.py

Simple code that implements the nested sampling to compute the evidence D-dimensiona gaussian. The posterior distributions of the parameters are also calculated. It was always assumed that the a priori distribution was uniform according to the indifference principle.

fit_nested_sampling.jl

Code for fitting data using nested sampling. In this case, as an example, we wanted to use the same data for the fit_pol_ran.py code and also the same function as a theoretical model. Any changes for other models are not particularly complicated. The calculations of the average values ​​of the parameters are calculated starting from the various posterior distributions for the sole purpose of carrying out a posterior predictive check.