This practice aims to data analysis and machine learning algorithms. Uncertainty quantification will be done mainly through Bayesian approach and will rely on computational statistics (Monte Carlo). Uncertainty means: getting systems to estimate how much they do not know. The practice will be focused more on practical aspects of uncertainty quantification, so that a new probabilistic programming methods (PyMC3) for modelling uncertainties are used. Topics that will be covered are related to:
Key words: Bayesian analysis, Uncertainty quantification, Probabilistic programming, Data analysis, Modeling, Monte Carlo analysis, Bayesian machine learning, Measurement, Errors...
Created by: Xunzhe Wen