Fix orphan in abstract

1 files changed, 3 insertions, 3 deletions

diff --git a/finale/final_report.tex b/finale/final_report.tex
index 522e3a7..6f943da 100644
--- a/finale/final_report.tex
+++ b/finale/final_report.tex
@@ -72,9 +72,9 @@ Diffusion Processes}
     The Network Inference Problem (NIP) is the machine learning challenge of
     recovering the edges and edge weights of an unknown weighted graph from the
     observations of a random contagion process propagating over this graph.
-    While previous work has focused on provable convergence guarantees for the
-    Maximum-Likelihood estimator for the edge weights, a Bayesian treatment of
-    the problem is still lacking. In this work, we establish a scalable Bayesian
+    While previous work has focused on provable convergence guarantees for
+    Maximum-Likelihood Estimation of the edge weights, a Bayesian treatment of
+    the problem is still lacking. In this work, we propose a scalable Bayesian
     framework for the unified NIP formulation of \cite{pouget}. Furthermore, we
     show how this Bayesian framework leads to intuitive and effective active
     learning heuristics which greatly speed up learning.