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. as soon as possible. 
—> Programming experience and knowledge of basic statistics required (e.g. PhD course “Statistical Methods in Physics”). Limited number of places.
Teacher: Martin Sahlén, Astronomy & Space Physics
Dates: Week 32 - 38 (6 August - 21 September 2018)
The course will be a mix of self-study, seminars, hands-on workshops, and lectures.
Statistical distributions
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
Preliminary course plan
Week 32 SS1
Statistical distributions
Maximum likelihood estimators
Bayesian parameter inference
  L1 Monte Carlo Markov Chain methods  
Week 33 W1
Monte Carlo Markov Chain parameter inference
Visualisation of results
Theoretical modelling
  • Covariance
  • Systematics
  • Hierarchical models
  • Priors
Bayesian model inference
Week 34 W2 Model building Compulsory
  H1 Hand-in: build and test a model Compulsory
Week 35 L2
Data-based modelling
  • Principal components analysis (PCA)
  • Gaussian processes
Methods for forecasting and optimisation of experiment performance
  W3 Model/data exploration Compulsory
Week 36 H2 Hand-in: exploration of model and data, future observations Compulsory
  S2 Handling large data sets: non-parametric methods, sparse methods, ...  
Week 37 W4 Hack Day  
Week 38 S3 Presentations of projects Compulsory
L = lecture, SS = self-study, S = seminar, W = workshop
The course will be based on tools in the python language, mainly:
Course literature
MacKay, D. Information Theory, Inference, and Learning Algorithms. Freely available at http://www.inference.org.uk/mackay/itprnn/book.html
Emcee documentation: https://arxiv.org/abs/1202.3665
    (more to be added)