Semester page for STK9190 - Autumn 2019

Joint teaching

This course has jointly taught classes with STK4190 – Bayesian nonparametrics. See this course's semester page for schedule and messages.

Messages

oral examination, Thursday December 19

Thanks for your efforts with the Exam Project. The follow-up oral examination part, with perhaps 20-25 minutes per candidate, is organised as follows, Thu Dec 19, in Room 1020 (Abel Building, tenth floor):

9:00 Dennis Christensen
9:30 William Denault
10:00 Maria Nareklishvili
10:30 Leiv Tore R?nneberg
11:00 Marthe Elisabeth Aastveit

Nils Lid Hjort & Anders L?land

Dec. 16, 2019 1:11 PM

exam!, parts I & II

1. The Exam Project will be made available on this course site, Wed Dec 4th, morning, and reports need to be handed in by Mon Dec 16th, at 10:59 or earlier; instructions are given on page 1 in the project description. Importantly, each report needs to contain *two extra special pages*: (i) a signed self-declaration form, essentially saying "I have worked on this by myself, and have duly referenced things & thangs I might have found in the libraries in the world, and I am not a plagiarist (etc.)"; and (ii) the student's one-page summary of the exam project, also containing a brief self-assessment of its quality. So if you feel that you've earned a clear A, say it (etc.).

2. Then on Thu Dec 19th there will be 25-minute oral examinations of the candidates; precise information about time schedule etc. will come in due time.

Good luck with your efforts!

Dec. 3, 2019 8:28 AM

Tue Nov 5: Gudmund Hermansen in loco parentis

I'm anderswo engagiert, on Tue Nov 5, but Gudmund Hermansen will teach, or perhaps rather give a mini-lecture, 9:15 to 11, with interruprtions and details, regarding a joint project of ours (which ought to be finished): Bayesian Nonparametrics for stationary Guassian time series. This is the thing: if y_1, y_2, ... are from such a time series, then there's a well-defined correlation function, which by the right theorem can be represented as

\corr(y_i, y_j) = 2 \int_0^\pi \cos( |j-i| \pi\omega)\,{{\rm d}}F(\omega,

for a suitable probability measure F on [0, \pi]. We may then put a Dirichlet process on this F, e.g. cented at the appropriate F_0 for an autoregressive process of some order, etc.

Thanks to Dennis Christensen for his 30-minute contribution last week; next week, Tue Nov 12, we're having

Maria Nareklishvili: Instrumental Variable Regression and its applications, Bayesian approach

Nov. 4, 2019 4:16 PM

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