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Computational Statistics [MA3402]

Course 820965689 in SS 2021

General Data

Course Type lecture
Semester Weekly Hours 2 SWS
Organisational Unit Research Department Mathematics Centre
Lecturers Donna Ankerst
Gregor Miller
Dates Tue, 10:15–11:45, virtuell

Further Information

Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.

additional remarks Parts of the following chapters from Rizzo ML. Statistical Computing with R, 2nd edition, CRC Press, 2019, available online. Ch.1 Introduction Ch.2 Probability and statistics review Ch.3 Methods for generating random variables Ch.4 Generating random processes Ch.5 Visualization of multivariate data Ch. 6 Monte Carlo integration and variance reduction Ch.7 Monte Carlo methods in inference Ch.8 Bootstrap and jackknife Ch.10 Permutation tests Ch.11 Markov Chain Monte Carlo methods Ch.14 Optimization
Links E-Learning course (e. g. Moodle)
TUMonline entry

Equivalent Courses (e. g. in other semesters)

SemesterTitleLecturersDates
SS 2020 Computational Statistics [MA3402] Czado, C. Hanebeck, A. Sahin, Ö. Tepegjozova, M. Wed, 15:15–16:45, virtuell
SS 2019 Computational Statistics [MA3402] Ankerst, D. Wed, 08:15–09:45, Interims II 004
SS 2018 Computational Statistics [MA3402] Czado, C. Wed, 15:15–16:45, BC2 BC2 0.01.17
and singular or moved dates
SS 2017 Computational Statistics [MA3402] Ankerst, D. Wed, 08:15–09:45, Interims I 101
SS 2016 Computational Statistics Czado, C. Thu, 10:15–11:45, BC1 BC1 2.02.01
and singular or moved dates
SS 2015 Computational Statistics Ankerst, D. Wed, 08:15–09:45, MI HS3
SS 2014 Computational Statistics Czado, C. Wed, 08:15–09:45, BC1 BC1 2.02.01
SS 2013 Computational Statistics Ankerst, D.
SS 2012 Computational Statistics Czado, C.
SS 2011 Computational Statistics Czado, C.
SS 2010 Computational Statistics
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