Advanced Methods in Applied Statistics 2019



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

Basic Information



  • Oral presentation and 1-2 page summary(10%)
  • Graded problem sets (20%)
  • Project (30%)
  • Final exam (40%)
  • Extra Credit (+2% to final course grade average on a 1-100% scale)

  • 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:

    Class 0 - Pre-Course

    Class 1 Start (Feb. 5)



    Class 2 - Monte Carlo Simulation & Least Squares (Feb. 7)



    Class 3 - Introduction to Likelihoods and Numerical Minimizers (Feb. 12)



    Class 4 - Intro. to Bayesian Statistics & Splines (Feb. 14)



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


    Class 6 - Markov Chain(s) (Feb. 21)



    Class 7 - Hypothesis Testing (Feb. 26)


    Class 8 - Data Driven Density Estimation (non-parametric) (Feb. 28)


    Class 9 - Confidence Intervals, Failures, and Feldman-Cousins (March 5)


    Class 10 - Presentations and Multivariate Analysis techniques (March 7)

    The Boosted Decision Tree lecture will be covered on March 14 in the afternoon due to the length of the excellent in-class student presentations and follow-up discussions.


    Class 11 - Divergence Between Distributions and Template Matching (March 12)


    Class 12 - Statistical Hypothesis Tests and Auto-Correlation (March 14)


    Class 10 - Statistical Hypothesis Tests and Auto-Correlation (March 14)


    Class 13 - Nested Sampling, Bayesian Inference, and MultiNest (March 19)


    Class 14 - Work on Project (no lecture or new material - March 21)


    Class 15 - Course Review, Discussion on Frequentist and Bayesian concepts, and Non-Parametric Tests Lecture snippet (March 26)



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