This is a course which will provide a first introduction to statistical methods in the context of data analysis of physical experiments and astrophysical observations. You will learn how to interpret data and confront it with theoretical models with modern statistical methods. After this course you will be able to independently select from a plethora of statistical tools to modern data analysis.
The confrontation of theories with data is at the core of modern sciences. In astrophysics state of the art statistical methods are used to assess and improve models. In this course you will learn from first principles the basics of Bayesian statistics. The course aims that you gain an intuition of which methods to apply in different situations. An important concept is the role of priors. It is only in comparison to prior knowledge that data can inform you about a model. In particular we will study the following topics:
The core of the course is not just the lecture. You will learn with hands on problem sheets to tackle statistical problems with the help of experienced tutors.
Please self enroll into the course and register on LSF here and find further information on Moodle.
Literature
The course will mainly follow the book:
"Data Analysis: A Bayesian Tutorial"
D.S. Sivia (with J. Skilling)
Oxford Science Publications
(available as e-book from the LMU library)
The statistical distributions are discussed in:
"Statistical Data Analysis"
G. Cowan
Oxford Science Publications
The MCMC chapter is inspired by:
"Markov Chain Monte Carlo in Practice"
W.R. Gilks, S. Richardson and D.J. Spiegelhalter
Chapman & Hall/CRC
A nice general introduction to probability:
Probability Theory - The Logic of Science
E. T. Jaynes
Cambridge Univeristy Press
(available as e-book from the LMU library)