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Modern Astrostatistics


Admission Requirements

Prior attendance of introductory statistics courses is helpful but not a mandatory prerequisite. Basic programming skills, or the will to develop these, are expected.


Randomness, uncertainties and deviations from the norm surround us in everyday life. A major asset of any scientist is to see beyond the complexity of noise, scatter and biases, and to find an underlying -often surprisingly simple- explanation for the noisy data. This course is specialized to astronomical data analysis, but the topics discussed will also foster an improved understanding of Google, Facebook and other free social media services.

Topics that will be covered include:

  • Descriptive statistics: Finding meaning in a huge data set.

  • Inference statistics: Constraining a physical model by data.

  • Filtering, e.g. for gravitational wave detections and source detection.

  • Random fields: Sky surveys and structure formation in cosmology.

  • Sampling methods: Making huge data analyses numerically feasible.

  • Bayesian Hierarchical Models: How to disentangle a seemingly complex analysis.

  • Prior Theory and Information Measures: How not to hide prejudices in an analyses.

  • Missing data and elusive physics: What to do if your sought signal hides in the dark figures?

  • Machine learning: Finding patterns which escape humans.

Course objectives

Principal course objective: After completion of this course, you will be able to correctly interpret noisy data. You will be able to design and apply statistical methods to answer scientific questions. You will be able to measure parameters, discover astronomical objects, or discover elusive signals in noisy data.

Upon completion of this course, you will be able to:

  • Recognize the most common distributions of noisy astronomical data

  • Identify signals in noisy data

  • Reject theories which are incompatible with data

  • Design own statistical methods to analyze complex data

  • Categorize astronomical objects

  • Solve simple Bayesian Hierarchical Models

  • Discover prejudices in analyses

  • Explain basic machine learning algorithms


See Astronomy master schedules

You will find the timetables for all courses and degree programmes of Leiden University in the tool MyTimetable (login). Any teaching activities that you have sucessfully registered for in MyStudyMap will automatically be displayed in MyTimeTable. Any timetables that you add manually, will be saved and automatically displayed the next time you sign in.

MyTimetable allows you to integrate your timetable with your calendar apps such as Outlook, Google Calendar, Apple Calendar and other calendar apps on your smartphone. Any timetable changes will be automatically synced with your calendar. If you wish, you can also receive an email notification of the change. You can turn notifications on in ‘Settings’ (after login).

For more information, watch the video or go the the 'help-page' in MyTimetable. Please note: Joint Degree students Leiden/Delft have to merge their two different timetables into one. This video explains how to do this.

Mode of instruction

Lectures on Monday, with bi-weekly numerical tutorial sessions on Thursday. One exercise sheet containing analytical and numerical problems will be handed out on Mondays. These are to be tackled proactively by the students. The Thursday tutorials will cover the analytical solutions, provide programming support, and help interpret the results.

Assessment method

  • Written exam , see the Astronomy master examination schedules
    The retake exam can be written, or oral, depending on the number of retake students. The oral exams are effectively still written exams: the examinee will have to solve exercises similar to the exam exercises, i.e. provide derivations, provide sketches, explain calculations, answer context questions.

Reading list



From the academic year 2022-2023 on every student has to register for courses with the new enrollment tool MyStudyMap. There are two registration periods per year: registration for the fall semester opens in July and registration for the spring semester opens in December. Please see this page for more information.

Please note that it is compulsory to both preregister and confirm your participation for every exam and retake. Not being registered for a course means that you are not allowed to participate in the final exam of the course. Confirming your exam participation is possible until ten days before the exam.

Extensive FAQ's on MyStudymap can be found here.


Lecturer: Dr. Elena Sellentin
Teaching Assistant: Erik Osinga


Soft skills
In this course, you will be trained in the following behaviour-oriented skills:

  • Problem solving (recognizing and analyzing problems, solution-oriented thinking)

  • Critical thinking (asking questions, check assumptions)

  • Analytical skills (analytical thinking, abstraction, evidence)

  • Creative thinking (resourcefulness, curiosity, thinking out of the box)