minor fixes

slides/BDA_lecture_10a.tex

@@ -334,7 +334,7 @@
     \begin{list2}
     \item<2-> the expected utility is 5€ for\\
       a) 100\% of receiving 5€\\
-      b) 50\% of losing 1M€ and 50\% of winning 1.00001M€
+      b) 50\% of losing 1M€ and 50\% of winning 1M€ + 10€
     \item<3-> most gambling has negative expected utility\\
       (but the excitement of the game may have positive utility)
     \end{list2}
@@ -398,7 +398,7 @@
     \end{list2}
   \item<3-> Example 2
     \begin{list2}
-    \item Imagine that in bioassay the posterior uncertainty of LD50 is too large
+    \item imagine that in bioassay the posterior uncertainty of LD50 is too large
     \item which dose should be used in the next experiment to reduce
       the variance of LD50 as much as possible ?
       \begin{list3}
@@ -432,17 +432,17 @@
 \begin{frame}{Bayesian optimization of wing length}
 
 \only<1>{Start with a small number of experiments\\
-\hspace{-8mm}\includegraphics[width=11.5cm]{helicopter_bo_initial_data.pdf}}
+  \hspace{-8mm}\includegraphics[width=11.5cm]{helicopter_bo_initial_data.pdf}\\}
 \only<2>{Gaussian process model\\
-\hspace{-8mm}\includegraphics[width=11.5cm]{helicopter_bo_initial_fit.pdf}}
+\hspace{-8mm}\includegraphics[width=11.5cm]{helicopter_bo_initial_fit.pdf}\\}
 \only<3-4>{Gaussian process model -- posterior draws\\
-  \hspace{-8mm}\includegraphics[width=11.5cm]{helicopter_bo_initial_fit_draws.pdf}}
+  \hspace{-8mm}\includegraphics[width=11.5cm]{helicopter_bo_initial_fit_draws.pdf}\\}
 \only<5>{Gaussian process model -- Thompson sampling\\
-  \hspace{-8mm}\includegraphics[width=11.5cm]{helicopter_bo_a_1.pdf}}
+  \hspace{-8mm}\includegraphics[width=11.5cm]{helicopter_bo_a_1.pdf}\\}
 \only<6>{Gaussian process model -- Thompson sampling\\
-  \hspace{-8mm}\includegraphics[width=11.5cm]{helicopter_bo_b_1.pdf}}
+  \hspace{-8mm}\includegraphics[width=11.5cm]{helicopter_bo_b_1.pdf}\\}
 
-{\vspace{-2\baselineskip}}
+{\vspace{-0.5\baselineskip}}
 \begin{list1}
   \item<4-> Thompson sampling:
     \begin{list2}

slides/BDA_lecture_10b.tex

@@ -535,7 +535,7 @@
   \item<+-> Normal approximation is not good for parameters with
     bounded or half-bounded support
     \begin{itemize}
-    \item e.g. $\theta \in [0,1]$ presentin probability
+    \item e.g. $\theta \in [0,1]$ presenting probability
     \item<+-> Stan code can include constraints\\
       \texttt{real<lower=,upper=0> theta;}
     \item<+-> for this, Stan does the inference in unconstrained space
@@ -676,14 +676,14 @@
         \begin{itemize}
         \item CS-E4820 - Machine Learning: Advanced Probabilistic Methods
         \item CS-E4895 - Gaussian Processes
-        \item Stan has the ADVI algorithm (not very good implementaion)
+        \item Stan has the ADVI algorithm (not very good implementation)
         \item Stan has Pathfinder algorithm (CmdStanR github version)
         \item instead of normal, methods with flexible flow transformations
         \end{itemize}
       \item expectation propagation  (Ch 13)
       \item speed of these is usually between optimization and MCMC
         \begin{itemize}
-        \item stochastic variational inference can be eeven slower than MCMC
+        \item stochastic variational inference can be even slower than MCMC
         \end{itemize}
       \end{itemize}
     \end{itemize}