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-implementation: PyMC (scalability), blocks
+In this section, we apply the framework from Section~\ref{sec:bayes}
+and~\ref{sec:active} on synthetic graphs and cascades to validate the Bayesian
+approach as well as the effectiveness of the Active Learning heuristics.
+
+We started with using the library PyMC to sample from the posterior distribution
+directly. This method was shown to scale poorly with the number of nodes in the
+graph, such that graphs of size $\geq 100$ could not be reasonably be learned
+quickly. In Section~\ref{sec:appendix}, we show the progressive convergence of
+the posterior around the true values of the edge weights of the graph for a
+graph of size $4$.
+
+In order to show the effect of the active learning policies, we needed to scale
+the experiments to graphs of size $\geq 1000$, which required the use of the
+variational inference procedure. A graph of size $1000$ has $1M$ parameters to
+be learned ($2M$ in the product-prior in Eq.~\ref{eq:gaussianprior}). The
+maximum-likelihood estimator converges to an $l_\infty$-error of $.05$ for most
+graphs after having observed at least $100M$ distinct cascade-steps.
+
 
 baseline
 
+fair comparison of online learning
+
 graphs/datasets
 
 bullshit