diff options
Diffstat (limited to 'finale/final_report.tex')
| -rw-r--r-- | finale/final_report.tex | 6 |
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. |