Fitting a model to data. The talk covers fitting a line model to a few data points using maximum likelihood and Markov Chain Monte Carlo methods.
Useful links for further reading:
A Jupyter notebook which containins the code needed to reproduce the figures in the talk. The notebook also shows how to implement maximum likelihood fitting of linear models for data with Gaussian noise, how to implement a simple Metropolis-Hasting MCMC sampler and how to use an advanced ensamble MCMC sampler emcee. https://github.com/fbartolic/fitting_model_to_data/blob/master/fitting_model_to_data.ipynb