Astrostats-2017-ESAC

The R notebooks provided here are superseded by those in my 2018 parallax tutorial.

Coryn Bailer-Jones, MPIA Heidelberg (https://mpia.de/homes/calj)

The PDF file astrostats_2017_ESAC.pdf contains the slides from the lecture, covering a quick overview of Gaia astrometry, GDR1, and the background theory on distance inference, as used in the notebooks.

There are three python notebooks with exercises which illustrate the statistical methods covered in the lectures. In your root directory put the notebooks (.ipynb) into a directory called "notebook/", the R codes (.R) into a directory called "Rcode/", and the data (*.csv) in a directory called "data/". The root directory is then defined in the first line of each notebook.

Resources:

• Gaia Data Release 1
• Open clusters in GDR1
• parallax paper 1
• parallax paper 2
• parallax paper 3
• Practical Bayesian Inference