Calendar

September 2018
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get an ICS file here of our seminars for your personal calendar

Special announcement

Gravitational waves have become a very hot topic in astrophysics since their detection by LIGO in 2015. This means that also possible precursors are in the focus of research – general relativistic research because these objects are either black holes or neutron stars. The 2017 Biermann Lecturer, Masaru Shibata from the Kyoto University, uses numerical simulations and general relativity (or numerical relativity for short) to study the merger of such extreme objects and the properties of both the electromagnetic radiation and gravitational waves emitted during these events.

Biermann Lectures 2017: Neutron star mergers and gravitational waves

September 04, 2017

Gravitational waves have become a very hot topic in astrophysics since their detection by LIGO in 2015. This means that also possible precursors are in the focus of research – general relativistic research because these objects are either black holes or neutron stars. The 2017 Biermann Lecturer, Masaru Shibata from the Kyoto University, uses numerical simulations and general relativity (or numerical relativity for short) to study the merger of such extreme objects and the properties of both the electromagnetic radiation and gravitational waves emitted during these events.

Regular Seminars

Monday  15:30 (weekly)    
Institute Seminar

MPA Lecture Hall    

Tuesday  11:00 (weekly)
Cosmology/Group Meeting     
MPA Lecture Hall    

Wednesday  11:00 (weekly)
SESTAS (Seminar on Stellar Astrophysics)     
MPA Room 005   

Wednesday  11:00 (weekly)
Galaxy group meeting (internal)    
MPA Room 006

Thursday  15:15 (weekly)    
Munich Joint Astronomy Colloquium
ESO Lecture Hall    

Friday  11:00 (weekly)    
Accretion and High Energy Astrophysics   
MPA Lecture Hall    

Last Friday of the month 14:00 (monthly)
Bayes Forum
MPA New Lecture Hall

Seminars and Lectures

Upcoming Seminars and Lectures

Bayes Forum - Talk

14661 1531234713

Probabilistic Numerics — Uncertainty in Computation

The computational complexity of inference from data is dominated by the solution of non-analytic numerical problems (large-scale linear algebra, optimization, integration, the solution of differential equations). But a converse of sorts is also true — numerical algorithms for these tasks are inference engines! They estimate intractable, latent quantities by collecting the observable result of tractable computations. Because they also decide adaptively which computations to perform, these methods can be interpreted as autonomous learning machines. This observation lies at the heart of the emerging topic of Probabilistic Numerical Computation, which applies the concepts of probabilistic (Bayesian) inference to the design of algorithms, assigning a notion of probabilistic uncertainty to the result even of deterministic computations. I will outline how this viewpoint is connected to that of classic numerical analysis, and show that thinking about computation as inference affords novel, practical answers to the challenges of large-scale, big data, inference. [more]

 
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