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The answers to the problems on Problem Set 12 can be found here:
/studier/emner/matnat/math/STK4021/h14/exercises/set2014_12_solutions.pdf
The last problem set (nr. 12) is now available on the course website. These problems will be discussed in the last class on December 5.
The syllabus page for the course has been updated to list all the topics covered in the lectures as well as the corresponding chapters in the lecture notes and in the book.
Everything listed as syllabus is on the reading list for the exam. If you haven't got a copy of the lecture notes, please send me an email.
I have also handed out answers to the written exercises on the problem sets. If you want a copy, you need to come by my office. Please send me an email beforehand to make sure I am in the office.
There will not be a lecture on November 28, as the take-home project is due that week.
The last lecture of the semester will thus be on December 5. In this lecture, we will go through Problem Set 12 which will be a set of review exercises. The problem set will be posted on the course website next week.
In today's lecture, we finished covering empirical Bayes inference thereby finishing the syllabus for the course.
In the 13th lecture on November 14, we finished our discussion of decision theory and started coverging empirical Bayes inference which is the topic of Chapter 5.1-5.2 in the book.
In the 12th lecture on November 7, we discussed some basic concepts from decision theory. This topic is covered in Appendix B in the book.
In the 11th lecture on October 31, we covered group comparisons and hierarchical modeling. If you don't have them already, you can get some additional notes on this topic from me.
The R code for Problem Sets 8-10 is available at
/studier/emner/matnat/math/STK4021/h14/exercises/set2014_8.r
/studier/emner/matnat/math/STK4021/h14/exercises/set2014_9.r
/studier/emner/matnat/math/STK4021/h14/exercises/set2014_10.r
The data set for Problem 1 on Problem Set 9 is available at
Today we discussed linear regression which is the topic of Chapter 4.1.1 in the book.
Today, we finished our discussion of the multivariate normal distribution and the Wishart distribution. The Wishart distribution is briefly discussed in Example 7.2 in the book. We also discussed multiple imputation under a multivariate normal model.
If you were not in class, you can contact me for some notes on this material.
This R-code is availble at
/studier/emner/matnat/math/STK4021/h14/exercises/set2014_6.r
The data sets for problem 2 on problem set 7 are available at
/studier/emner/matnat/math/STK4021/h14/exercises/bluecrab.dat
and
/studier/emner/matnat/math/STK4021/h14/exercises/orangecrab.dat
R-code to sample Wishart random variables can be found at
/studier/emner/matnat/math/STK4021/h14/exercises/set2014_7_start.r
Update: The R-code for the analysis is available at
/studier/emner/matnat/math/STK4021/h14/exercises/set2014_7.r
We finished covering asymptotic methods which is the topic of Chapter 3.2 in the book. We further started with the topic of Chapter 4.1, Bayesian modeling. We will cover this topic in a somewhat more detail than the book, including today's discussion of the multivariate normal distribution.
Today, I also handed out the lecture notes from the first half of the class (lectures 1-6). If you weren't there today, you can get a copy next Friday or send me an email.
The R-code for problem 2 on Problem Set 5 is available at
/studier/emner/matnat/math/STK4021/h14/exercises/set2014_5.r
In this lecture, we finished discussing convergence diagnostics (Chapters 3.4.5-3.4.6 in the book) and started our discussion of asymptotic methods (Chapter 3.2 in the book).
In today's lecture, we finished the discussion of MCMC algorithms (Chapters 3.4.1-3.4.2 in the book) and started discussing convergence diagnostics (Chapters 3.4.5-3.4.6 in the book).
In the fifth lecture on September 19, we finished our discussion of probit regression models and defined the Metropolis algorithm which is the topic of Chapter 3.4.2 in the book.
The data set for Problem 2 on Problem Set 4 is available at
/studier/emner/matnat/math/STK4021/h14/exercises/msparrownest.dat
Update: The R code is now available at
/studier/emner/matnat/math/STK4021/h14/exercises/set2014_4.r
In today's lecture, we finished covering the Gibbs sampler (Chapter 3.4.1 in the book) and started the discussion of a probit regression example.
The two data sets needed to solve the exercises on Problem Set 3 are
/studier/emner/matnat/math/STK4021/h14/exercises/glucose.dat
and
/studier/emner/matnat/math/STK4021/h14/exercises/divorce.dat
Update: R-code for the problems on Problem Set 3 is available at
/studier/emner/matnat/math/STK4021/h14/exercises/set2014_3.r
In today's lecture, we discussed Bayes factors, AIC and BIC which concludes Chapter 2.3 in the book. Furthermore, we started discussing Markov chain Monte Carlo methods which are the subject of Chapter 3.4 in the book.
Two data sets are needed for Exercise 3 on Problem Set 2:
/studier/emner/matnat/math/STK4021/h14/exercises/menchild30bach.dat
/studier/emner/matnat/math/STK4021/h14/exercises/menchild30nobach.dat
Update: The R code for Problem Set 2 is available at
/studier/emner/matnat/math/STK4021/h14/exercises/set2014_2.r