Advanced Methods in Applied Statistics 2017



Lecturer: D. Jason Koskinen
Email: koskinen (at) nbi.ku.dk

Basic Information



Evaluation


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.