Advanced Methods in Applied Statistics 2020

Lecturer: D. Jason Koskinen
Email: koskinen (at)

Basic Information


The presentation, the problems sets, and the project will all be submitted and assigned from Absalon. So check Absalon for instructions and due dates. The final exam is handled by the eksamen webpage.

Course Syllabus

The course is 100% likely to change once we begin, and future lectures listed below serve as an outline. Even so, we are very likely to cover the following topics which may require additional software support:

Class notes will be posted here on this webpage as they become available.

Class 0 - Pre-Course (Jan. 30)
Class 1 - Start (Feb. 4)

Class 2 - Monte Carlo Simulation & Least Squares (Feb. 6)

Class 3 - Introduction to Likelihoods and Numerical Minimizers (Feb. 11)

Class 4 - Intro. to Bayesian Statistics & Splines (Feb. 13)

Class 5 - Parameter Estimation and Confidence Intervals (Feb. 18)

Class 6 - Markov Chain(s) (Feb. 20)

Class 7 - Hypothesis Testing (Feb. 25)

Class 8 - Data Driven Density Estimation (non-parametric) (Feb. 27)

Class 9 - Statistical Hypothesis Tests and Auto-Correlation (March 3)

Class 10 - Presentations and Multivariate Analysis techniques (March 5)

The Boosted Decision Tree lecture

Class 11 - Neural likelihood-free inference (March 10)

Class 12 - Continuation of likelihood-free inference (March 12)

Class 13 - Nested Sampling, Bayesian Inference, and MultiNest (March 17)

Class 14 - Work on Project (no lecture or new material - March 19)

Class 15 - Course Review, and Non-Parametric Tests Lecture snippet (March 24)

Extra Projects of a more difficult nature, for those who want something more challenging.