Advanced Methods in Applied Statistics 2017

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

Basic Information


Course Syllabus

The outline is a rough sketch of the course material, and is 100% likely to change throughout the course. Even so, we are very likely to cover the following topics which may require additional software support:

Class notes will be posted here:

Class 0 - Pre_Course, attendance is not required

Class 1 Start

Class 2 - Monte Carlo Simulation & Least Squares regression

Class 3 - Introduction to Likelihoods and Numerical Minimizers

Class 4 - Finish Introduction Likelihoods and Minimizers, then Intro. to Bayesian Statistics

Class 5 - Background Subtraction and sPlots

Class 6 - Markov Chain(s)

Class 7 - Parameter Estimation and Confidence Intervals

Class 8 - Hypothesis Testing

Class 9 - Interpolation and Splines

Class 10 - Presentations and Multivariate Analysis techniques

Class 11 - Data Driven Density Estimation (non-parametric)

Class 12 - Confidence Intervals, Failures, and Feldman-Cousins

Class 13 - Nested Sampling, Bayesian Inference, and MultiNest

Class 14 - Signal and Data Processing (Wavelets)

Class 15 - Non-Parametric Tests Lecture snippet and Course Review

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