Bayesian Data Analysis D
CS-E5710 • 5 op • Aki Petri Vehtari
Hyödyllisyys
Työmäärä
Sisältö
Bayesian probability theory and bayesian inference. Bayesian models and their analysis. Computational methods, Markov-Chain Monte Carlo.
Osaamistavoitteet
After the course, the student can explain the central concepts in Bayesian statistics, and name steps of the Bayesian modeling process. The student can recognize usages for common (i.e. those presented during the course) statistical models, and formulate the models in these situations. The student can compare the most popular Bayesian simulation methods, and implement them. The student can use analytic and simulation based methods for learning the parameters of a given model. The student can estimate the fit of a model to data and compare models.
Esitiedot
Differential and integral calculus, basics of probability and statistics, basics of programming (R or Python). Recommended: matrix algebra.
Työmäärä
Lectures 10x2h, computer exercises 10x2h, independent studying (text book, programming, home assignment and project reports), project presentation
Arviointimenetelmät
Assignments (67%) and a final project work with presentation (33%). Minimum of 50% of points must be obtained from both the assignments and the project work.