,...Merge branch 'master' of https://version.helsinki.fi/rikulain/bachelors-thesis

paper/imputation.tex

@@ -208,7 +208,7 @@ In particular, we consider the full probabilistic model defined in Equations \re
 % 
 %Notice that by ``parameters'' here we refer to all quantities that are not considered as known with certainty from the input, and so parameters include unobserved features \unobservable.
 %
-We use prior distributions given in Appendix~X to ensure the identifiability of the parameters.
+We use prior distributions given in Appendix~X to ensure the identifiability of the parameters.
 %Formally, we obtain the parameter posterior by
 %\begin{equation}
 %	\prob{\parameters | \dataset} = \frac{\prob{\dataset | \parameters} \prob{\parameters}}{\prob{\dataset}} .

paper/sl.tex

@@ -101,8 +101,7 @@ The approach is also demonstrated to be robust to different variations in the de
 
 \input{imputation}
 
-%\input{experiments} 
-%Antti: took out to compile, a figure is missing
+\input{experiments} 
 
 \input{related}