Teacher: Martin Sahlén, Astronomy - This email address is being protected from spambots. You need JavaScript enabled to view it.

Dates: Week 32 - 38 (6 August - 21 September 2018)

The course will be a mix of self-study, seminars, hands-on workshops, and lectures. 

A fairly compressed, hands-on course with the explicit aim of allowing the participants to work on a current research problem of their own and gain directly relevant skills, tools and results alongside an overview of the topic. The content will be geared towards astronomy/astrophysics, but should be relevant and accessible for anyone using data to test parametric models using computational methods. Examination consists of two hand-ins, workshop participation and an oral project report. You are also welcome to attend parts of the course “not for credit”. 

If you are interested in attending, please contact This email address is being protected from spambots. You need JavaScript enabled to view it.
Programming experience and knowledge of basic statistics required (e.g. PhD course “Statistical Methods in Physics”). Limited number of places.

Syllabus
Statistical distributions. Estimators. Bayesian parameter inference. Bayesian model inference. Computational inference, Monte Carlo. Visualisation and interpretation of inference products. Statistical modelling of observations. Model compression. Data compression. Forecasting observational performance, observational design.
 
Slack channel: statinf18.slack.com - join for discussion, help, extra material!
 
Course documents
Course plan NB: room changes, see schedule link! Hand-in 2 deadline extended to 16 September.
 
Hand-ins
Lectures
  • Lecture 1: Monte Carlo Markov Chain methods. See Slack #lectures for lecture notes.
  • Lecture 2: Data-based modelling, forecasting and optimisation of experiments. Course handout preparatory reading. See Slack #lectures for lecture notes.
  • Extra lecture: MCMC and inference mechanics in more detail. See Slack #lectures for lecture notes.
Workshops
Seminars
Software
The course will be based on tools in the python language, mainly:
 
Course literature