The 11th IMPRS Summer School

 

This webpage provides information for the 11th Heidelberg Summer School on the topic of
Astrostatistics & Data Mining

The 11th Heidelberg Summer School will take place September 12-16, 2016 at the "Max-Planck-Haus" (Max Planck House) in Heidelberg, Germany.

Organisation:

IMPRS for Astronomy and Cosmic Physics at the University of Heidelberg:

Max Planck Institute for Astronomy, Max Planck Institute for Nuclear Physics, Astronomisches Rechen-Institut, Institute for Theoretical Astrophysics, Landessternwarte Koenigstuhl, and Heidelberg Institute for Theoretical Studies.

Scientific organizing committee:   Coryn Bailer-Jones  (MPIA, Heidelberg).

Invited lecturers:

   Dan Foreman-Mackey  (University of Washington)

   Daniela Huppenkothen  (New York University)

   Robert Lupton  (Princeton University)

   Benjamin Wandelt (Institute for Astrophysics, Paris)

Scope of the School:

Astronomical data sets are increasing in size and complexity. Extracting meaningful and reliable scientific results from these is a challenge, and relies on the application of appropriate and properly-understood methodology. 

The school looked at the principles of inference and methods of astronomical data analysis and data mining, also covering a range of numerical and statistical techniques and their application to different types of astronomical data.

The emphasis was on practical methodology and solving real-world problems to address questions like: How do we deal with missing data, outliers, and selection effects? How should we compare models to data? How do we best combine different data sets? What is an efficient numerical approach? Topics that have been covered include:

- time series analysis, including quasi-periodicity and stochastic processes
- low photon count data
- multi-dimensional interpolation
- image processing: optimal addition and subtraction
- non-Gaussian and correlated noise
- Bayesian model comparison vs. orthodox hypothesis testing
- MCMC methods
- hierarchical models
- parameter estimation in high dimensional problems
- novelty detection
- handling and visualizing large or multi-dimensional data sets

           

School format:

The school had four main components spread throughout the week

1.  A series of structured lectures given by the four lecturers.

2.  Computer-based exercises based on the topics given in the lectures. Some lecturers will integrate the exercise sessions into their lectures.

3.  Presentations by other speakers on the application of astrostatistics  to open scientific problems.

4.  A social programme to enable and encourage scientific interaction between students, lecturers and speakers.

Have a look to our   program   (final, as of Sept.9, 2016).

 
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