diff --git a/slides/talks/2017-1-EDBT-Inference/index.html b/slides/talks/2017-1-EDBT-Inference/index.html
index e7fdeb71..9e9e645c 100644
--- a/slides/talks/2017-1-EDBT-Inference/index.html
+++ b/slides/talks/2017-1-EDBT-Inference/index.html
@@ -71,11 +71,11 @@
 			</section>
 
 			<section>
-				<img src="graphics/yingyang.jpg" style="float: right; margin-left: 20px;" />
+				<img src="graphics/yingyang.jpg" style="float: right; margin-left: 20px; padding-top: 20px" />
 				<h3>Disclaimer</h3>
 				<p>Ying could not be here today. If you like her ideas, get in touch with her. </p>
-				<p class="fragment">(she's on the job market)</p>
 				<p class="fragment">(If you don't, blame my presentation)</p>
+				<p class="fragment">(Also, Ying is on the job market)</p>
 			</section>
 		</section>
 
@@ -93,9 +93,9 @@
 				<h2>Graphical Models</h2>
 				<p>Joint probability distributions are expensive to store<br/>
 				$$p(D, I, G, S, J)$$</p>
-				<p>Bayes rule lets us break apart the distribution<br/>
+				<p class="fragment">Bayes rule lets us break apart the distribution<br/>
 				$$= p(D, I, G, S) \cdot p(J | D, I, G, S)$$</p>
-				<p>And conditional independence lets us further simplify<br/>
+				<p class="fragment">And conditional independence lets us further simplify<br/>
 				$$= p(D, I, G, S) \cdot p(J | G, S)$$</p>
 				<p class="fragment">This is basis for a type of graphical model called a "Bayes Net"</p>
 			</section>
@@ -136,6 +136,7 @@
 					<span class="fragment" data-fragment-index="3">$\cdot\;0.95$</span>
 					<span class="fragment" data-fragment-index="4">$\cdot\;0.25$</span>
 					<span class="fragment" data-fragment-index="5">$\cdot\;0.8$</span>
+					<span class="fragment" data-fragment-index="6">$=\;0.0665$</span>
 				</p>
 			</section>
 			<section>
@@ -178,7 +179,7 @@
 			</section>
 
 			<section>
-				<h2>Idea: Use OLA</h2>
+				<h2>Idea: Online Aggregation</h2>
 			</section>
 			<section>
 				<h2>Online Aggregation (OLA)</h2>
@@ -260,17 +261,6 @@
 				</ol>
 			</section>
 
-			<section>
-				<h2>Cyclic Sampling</h2>
-				<dl>
-					<dt>Advantages</dt>
-						<dd>Progressively better estimates over time.</dd>
-						<dd>Converges in bounded time.</dd>
-					<dt>Disadvantages</dt>
-						<dd>Exponential time in the number of variables.</dd>
-				</dl>
-			</section>
-
 			<section>
 				<h2>Accuracy</h2>
 				<dl>
@@ -281,6 +271,17 @@
 				</dl>
 				<p></p>
 			</section>
+
+			<section>
+				<h2>Cyclic Sampling</h2>
+				<dl>
+					<dt>Advantages</dt>
+						<dd>Progressively better estimates over time.</dd>
+						<dd>Converges in bounded time.</dd>
+					<dt>Disadvantages</dt>
+						<dd>Exponential time in the number of variables.</dd>
+				</dl>
+			</section>
 		</section>
 
 		<section>
@@ -293,18 +294,18 @@
 				<img src="graphics/JoinGraph.svg" height="400" style="float:left"/>
 				<table style="float:right">
 					<tr><th>$G$</th><th>#</th><th>$\sum p_{\psi_2}$</th></tr>
-					<tr class="fragment" data-fragment-index="2"><td>1</td><td>3</td><td>0.348</td></tr>
-					<tr class="fragment" data-fragment-index="3"><td>2</td><td>4</td><td>0.288</td></tr>
-					<tr class="fragment" data-fragment-index="3"><td>3</td><td>4</td><td>0.350</td></tr>
+					<tr class="fragment" data-fragment-index="2"><td>1</td><td>1</td><td>0.126</td></tr>
+					<tr><td>&nbsp;</td><td>&nbsp;</td><td>&nbsp;</td></tr>
+					<tr><td>&nbsp;</td><td>&nbsp;</td><td>&nbsp;</td></tr>
 				</table>
 				<table>
-					<tr><th>$D$</th><th>$G$</th><th>#</th><th>$\sum p_{\psi_1}$</th></tr>
-					<tr class="fragment" data-fragment-index="1"><td>0</td><td>1</td><td>1</td><td>0.126</td></tr>
-					<tr class="fragment" data-fragment-index="3"><td>1</td><td>1</td><td>2</td><td>0.222</td></tr>
-					<tr class="fragment" data-fragment-index="3"><td>0</td><td>2</td><td>2</td><td>0.238</td></tr>
-					<tr class="fragment" data-fragment-index="3"><td>1</td><td>2</td><td>2</td><td>0.050</td></tr>
-					<tr class="fragment" data-fragment-index="3"><td>0</td><td>3</td><td>2</td><td>0.322</td></tr>
-					<tr class="fragment" data-fragment-index="3"><td>1</td><td>3</td><td>2</td><td>0.028</td></tr>
+					<tr><th>$I$</th><th>$G$</th><th>#</th><th>$\sum p_{\psi_1}$</th></tr>
+					<tr class="fragment" data-fragment-index="1"><td>0</td><td>1</td><td>1</td><td>0.18</td></tr>
+					<tr><td>&nbsp;</td><td>&nbsp;</td><td>&nbsp;</td><td>&nbsp;</td></tr>
+					<tr><td>&nbsp;</td><td>&nbsp;</td><td>&nbsp;</td><td>&nbsp;</td></tr>
+					<tr><td>&nbsp;</td><td>&nbsp;</td><td>&nbsp;</td><td>&nbsp;</td></tr>
+					<tr><td>&nbsp;</td><td>&nbsp;</td><td>&nbsp;</td><td>&nbsp;</td></tr>
+					<tr><td>&nbsp;</td><td>&nbsp;</td><td>&nbsp;</td><td>&nbsp;</td></tr>
 				</table>
 			</section>
 
@@ -317,7 +318,7 @@
 					<tr><td>3</td><td>4</td><td>0.350</td></tr>
 				</table>
 				<table>
-					<tr><th>$D$</th><th>$G$</th><th>#</th><th>$\sum p_{\psi_1}$</th></tr>
+					<tr><th>$I$</th><th>$G$</th><th>#</th><th>$\sum p_{\psi_1}$</th></tr>
 					<tr><td>0</td><td>1</td><td>2</td><td>0.140</td></tr>
 					<tr><td>1</td><td>1</td><td>2</td><td>0.222</td></tr>
 					<tr><td>0</td><td>2</td><td>2</td><td>0.238</td></tr>
@@ -336,7 +337,7 @@
 					<tr><td>3</td><td>4</td><td>0.350</td></tr>
 				</table>
 				<table>
-					<tr><th>$D$</th><th>$G$</th><th>#</th><th>$\sum p_{\psi_1}$</th></tr>
+					<tr><th>$I$</th><th>$G$</th><th>#</th><th>$\sum p_{\psi_1}$</th></tr>
 					<tr><td>0</td><td>1</td><td>2</td><td>0.140</td></tr>
 					<tr><td>1</td><td>1</td><td>2</td><td>0.222</td></tr>
 					<tr><td>0</td><td>2</td><td>2</td><td>0.238</td></tr>
@@ -429,6 +430,7 @@
 				<li><b>Idea 2</b>: "Leak" samples through a normal join graph.</li>
 				<li>Compared to both Variable Elim. and Gibbs Sampling, Leaky Joins are often better and never drastically worse.</li>
 			</ul>
+			<p class="fragment" style="font-weight: bold">Questions?</p>
 		</section>
 
 		</div></div>