Data Modeling for the Natural Sciences

Catalog description

This self-contained course covers data modeling tailored for students in the sciences. Starts from basic probabilistic concepts, models of dynamical systems, stochastic processes, likelihoods, frequentist and Bayesian inference, as well as Monte Carlo samplers and filtering. Develops modeling paradigms, including mixture models, regression models, Gaussian processes, hidden Markov models, state-space models and Kalman filtering.