Dr. Jonathan Gair
Dr. Jonathan Gair
Group Leader
Phone:+49 331 567-7305

Homepage of Jonathan Gair

Lecture dates

Dates of the lectures:

Nov 20, 2019 11:30am — 12:30pm
Nov 22, 2019 11:30am — 12:30pm
Nov 27, 2019 11:30am — 12:30pm
Nov 29, 2019 11:30am — 12:30pm
Dec 4, 2019 11:30am — 12:30pm
Dec 6, 2019 11:30am — 12:30pm
Dec 11, 2019 11:30am — 12:30pm
Dec 13, 2019 11:30am — 12:30pm
Jan 15, 2020 11:30am — 12:30pm
Jan 17, 2020 11:30am — 12:30pm
Jan 22, 2020 11:30am — 12:30pm
Jan 24, 2020 11:30am — 12:30pm
Jan 29, 2020 11:30am — 12:30pm
Jan 31, 2020 11:30am — 12:30pm
Feb 5, 2020 11:30am — 12:30pm
Feb 7, 2020 11:30am — 12:30pm


Seminar room 0.01, AEI Potsdam

International Max Planck Research School on Gravitational-Wave Astronomy: Lectures for Graduate Students

The Astrophysical and Cosmological Relativity and Computational and Relativistic Astrophysics divisions of the AEI are part of the International Max Planck Research School (IMPRS) on Gravitational-Wave Astronomy. The divisions are organising a sequence of courses, targeted at graduate students, but open to all. The first course took place in Fall/Winter 2019/2020. The course was broadcast to all IMPRS partner institutions.

Making sense of data: introduction to statistics for gravitational-wave astronomy

Synopsis: Measurements of the properties of gravitational wave sources are imperfect due to the presence of noise in the gravitational wave interferometers used to detect them. Extracting useful scientific information from these observations therefore requires careful statistical analysis of the data in order to understand the significance of the observed events, the level of uncertainty in the parameter estimates and the implications of the observations for the population from which the sources are drawn. This lecture course gives an overview of some key statistical ideas and techniques that are essential for interpreting current and future gravitational wave observations.

Plan for lecture topics:

Week 1 & 2: Frequentist statistics
Lecture 1: Introduction to random variables, common probability distributions

Lecture 2: Basis statistical theory, including Cramer-Rao bound

Lecture 3: Hypothesis testing, Neyman-Pearson lemma, ROC curves

Practical 1: introduction to R

Week 3 & 4: Bayesian statistics
Lecture 4: Introduction to Bayesian statistics: Bayes theorem, prior choices

Lecture 5: Introduction to Bayesian statistics: Bayesian hypothesis testing, posterior predictive checking, hierarchical models

Lecture 6: Bayesian sampling methods

Practical 2: Introduction to JAGS

Week 5 & 6: Statistics in GW astronomy
Lecture 7: Stochastic processes, optimal filtering, signal-to-noise ratio, sensitivity curves

Lecture 8: Frequentist statistics in GW astronomy - FAR, Fisher Matrix, PSD estimation

Lecture 9: Bayesian statistics in GW astronomy - PE, population inference

Practical 3: GW population analysis

Week 7 & 8: Advanced topics
Lecture 10: Time series analysis - auto-regressive process, moving average processes, ARMA models etc.

Lecture 11: Nonparametric regression - kernel density estimation, smoothing splines, wavelets

Lecture 12: Gaussian processes, Dirichlet processes

Practical 4: Nonparametric curve fitting

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