Advanced Methods in Applied Statistics 2020
Lecturer: D. Jason Koskinen
Email: koskinen (at) nbi.ku.dk
Basic Information
- Block 3 - Timetable A of the 2020 academic
calendar
- Tues 08:00 - 12:00 and Thurs 08:00- 12:00 & 13:00 - 17:00
- Actual
- 08:30 - 09:00 Q&A or discussion with Jason in the classroom
- 09:00 lecture on new material (not 09:05 or 09:15)
- On Thursday there will very often be new material starting at 13:00
- On Thursday it is very unlikely that any new material, lectures, or review will happen after 16:00.
- There are multiple locations depending on the day (timetable):
Tuesday is at C103, Universitetsparken 5, HCØ- Tuesday is at NBB.01.01.G076, Jagtvej 155
- Thursday morning is at NBB 01.3.I.164, Jagtvej 155
- Thursday afternoon is at AB Teori 2, Nørre Alle 55
- Odd-numbered classes are 4-hours while even-numbered consist of 2 blocks of 4-hours
- Classes will be composed of ~20-30% lecture and demonstrations followed by exercise
- While assignments, projects, and exercises can be done in the programming language of the students choice, the examples and demonstrations will be mainly in Python and/or scientific packages thereof, i.e. SciPy, PyROOT, etc.
- Required text or textbooks: None
- 2016 Advanced Methods in Applied Statistics webpage
- 2017 Advanced Methods in Applied Statistics webpage
- 2018 Advanced Methods in Applied Statistics webpage
- 2019 Advanced Methods in Applied Statistics webpage
- It is recommended, but not required, to have at least reviewed the little sibling to this course, i.e. "Applied Statistics - From data to results" which can be found here
Evaluation
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.
- Oral
presentation and 1-2 page summary (10%)
- Graded
problem sets (20%)
- Project
(30%)
- You may start working on this right now!!
- Final exam (40%)
- 28 hour take home exam
- The exam will be similar to problem set 2
- A handful of more intensive questions as opposed to numerous
short questions
- While the exam will contain problems from any portion of the
course material, the focus will be more on topics in the latter
portion of the course
- Here
are two extra practice problems and the exams for 2016 and 2017
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:
- Multivariate analysis (MVA) techniques including Boosted Decision
Trees (BDTs)
- The MultiNest bayesian inference tool
- Basis splines
- Markov Chain Monte Carlo
- Likelihood minimization techniques
Class notes will be posted here on this webpage as they become available.
Class 0 - Pre-Course (Jan. 30)
- The location is Fb-6 at Blegdamsvej from 13:00-16:00
- Take a look before the class starts (optional)
- Get a preview with the course Teaching Assistant (James Creswell) of
some software tools to install
Class 1 - Start (Feb. 4)
- Course Information
- Chi-square
- Code chi-square
- Data for exercise 1 (FranksNumbers.txt)
- Review of 'basic' statistics
- Lecture 1
- Be knowledgeable about the Central Limit Theorem
- Start reading paper about how well Gaussian statistics compares to a
wide selection of scientific measurements
- "Not Normal: the uncertainties of scientific measurements" link at arXiv
or DOI
- We will be discussion the paper in the next class, i.e. on Thursday
Class 2 - Monte Carlo Simulation & Least Squares (Feb. 6)
- Lecture 2
- Monte Carlo (reminder that lecture starts at 09:00)
- Code for area of the
circle
- Example code
from Jean-Loup in a Jupyter notebook
- From the "Not Normal: the uncertainties of scientific measurements" paper:
- For the ambitious, create a 'toy monte carlo' of the sample and pair
distributions for the nuclear physics data in Sec. 2.A. For simplicity
assume that all the 'quantities' are gaussian distributed
- Write functions where you can produce multiple gaussian
distributions to sample from and generate a sample of "12380
measurements, 1437 quantities, 66677 pairs".
- Produce the z-distribution (using eq. 4) plot for just your toy
monte carlo and see if it matches a gaussian, exponential, student-t
distribution, etc...
- Least Squares lecture (starting at 13:00)
- Some useful links
- Discussion of "Not Normal: the uncertainties of scientific
measurements" (arXiv or DOI)
Class 3 - Introduction to Likelihoods and Numerical Minimizers (Feb. 11)
- Remember that the first assignment is due on Wednesday
Class 4 - Intro. to Bayesian Statistics & Splines (Feb. 13)
- Lecture 4 on Simple Bayesian statistics
- Using priors, posteriors, and likelihoods
- Example code for
exercises from Jason
- Lecture 4.5
- Splines
- Data files for one of the exercises.
- Interesting article about use of splines and penalty terms
Class 5 - Parameter Estimation and Confidence Intervals (Feb. 18)
- Reminder: oral presentation and 1-2 page article reports will be due/covered soon
Class 6 - Markov Chain(s) (Feb. 20)
- Lecture 6 Markov Chain Monte Carlo (MCMC)
- Look for an external package for Markov Chain Monte Carlo (MCMC), e.g. emcee, PyMC
- Just like minimizers, syntax and options matter
- Be familiar with your chosen MCMC package
- Some example python code for the exercises (caveat emptor)
- Using emcee, the solution is graciously provided by Niccolo Maffezzoli (2017 TA)
Class 7 - Hypothesis Testing (Feb. 25)
- Lecture 7
- Likelihood ratio
- Data files for one of the exercises. Just use the first column in each file. The second column is unimportant.
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)
- In the morning we will have the oral presentations from the articles chosen
The Boosted Decision Tree lecture
- Boosted Decision Trees
- Lecture 10
- Data
- Exercise 2 (16 variable file)
- The first column is the index, hence there are 17 'variables', but the index variable only for book keeping and has no impact on whether an event is signal or background.
- Every even row is the 'signal' and every odd row is the 'background'. Thus, there are two rows for each index in the first column: the first is the signal and the second is the background. [Format is odd, but I got it from a colleague].
- Here is the solution data sets separated into two files (benign
and malignant) for the last exercise of the lecture. Here is also the (python) code that I used to
establish the efficiency for all the submissions from all the students
Class 11 - Neural likelihood-free inference (March 10)
- Guest Lecture by Christoph Weniger
- lecture notes
- Introduction to simple deep feed-forward networks
- Parameter regression
- Regression of likelihood-to-evidence ratios
Class 12 - Continuation of likelihood-free inference (March 12)
- Guest lecture by Christoph Weniger
- lecture notes
- Introduction to simple deep feed-forward networks
- Parameter regression
- Regression of likelihood-to-evidence ratios
Class 13 - Nested Sampling, Bayesian Inference, and MultiNest (March 17)
- Lecture 13
- External packages for conducting nested sampling, e.g. MultiNest, are necessary and some python options are:
- Very good articles that are easy to read
Class 14 - Work on Project (no lecture or new material - March 19)
Class 15 - Course Review, and Non-Parametric Tests Lecture snippet (March 24)
- Lecture 15 (EXTRA)
- Kolmogorov-Smirnov, Anderson-Darling, and Mann-Whitney U tests
- Won't be be covered in class
- Topics include things that may be useful for research
Extra Projects of a more difficult nature, for those who want something
more challenging.