Advanced Methods in Applied Statistics 2016

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 will absolutely cover the following topics which may require additional software support:

Slack Channel for communicating and sharing comments, question, plots, solutions, code, etc... Instructions are

Class notes will be posted here:

Class 0 - Pre_Course, attendance is not required
Class 1 Start
Class 2 - Monte Carlo Simulation

Class 3 - Method of Least Squares

Class 4 - Likelihoods and Numerical Minimization Fitting

Class 5 - Bayesian Statistics Introduction

Class 6 - Markov Chain Monte Carlo

Class 7 - Parameter Estimation

Class 8 - Hypothesis Testing

Class 9 - Splines

Class 10 - Oral presentations (in class) & Non-parametric Tests

Class 11 - Multi-Variate Analysis technique (MVA)

Class 12 - Data Processing and Signal Processing

Class 13 - Rare Events

Class 14 - Nested Sampling in Bayesian Inference

Class 15 - Background subtraction and sPlots

Class 16 - Review

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