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authorRoss Barnowski <rossbar@berkeley.edu>2020-12-12 13:01:43 -0800
committerGitHub <noreply@github.com>2020-12-12 13:01:43 -0800
commit1462350ebb1467a46af4f7b774bb093ce15eb6c6 (patch)
tree126ec15cd3a58da324fee9b419e7e73c7e2df8e1 /networkx/algorithms/d_separation.py
parent812f115e9b98bcefc8fdb8a3479e3cd32468ac5e (diff)
downloadnetworkx-1462350ebb1467a46af4f7b774bb093ce15eb6c6.tar.gz
DOC: Switch from napoleon to numpydoc sphinx extension (#4447)
* Setup for numpydoc. * Add to doc requirements. * Replace napoleon with numpydoc in conf.py. * DOC: Fixups from numpydoc.
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diff --git a/networkx/algorithms/d_separation.py b/networkx/algorithms/d_separation.py
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@@ -40,13 +40,20 @@ True
References
----------
-.. [1] Pearl, J. (2009). Causality. Cambridge: Cambridge University Press.
+.. [1] Pearl, J. (2009). Causality. Cambridge: Cambridge University Press.
-.. [2] Darwiche, A. (2009). Modeling and reasoning with Bayesian networks. Cambridge: Cambridge University Press.
+.. [2] Darwiche, A. (2009). Modeling and reasoning with Bayesian networks.
+ Cambridge: Cambridge University Press.
-.. [3] Shachter, R. D. (1998). Bayes-ball: rational pastime (for determining irrelevance and requisite information in belief networks and influence diagrams). In , Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (pp. 480–487). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.
+.. [3] Shachter, R. D. (1998).
+ Bayes-ball: rational pastime (for determining irrelevance and requisite
+ information in belief networks and influence diagrams).
+ In , Proceedings of the Fourteenth Conference on Uncertainty in Artificial
+ Intelligence (pp. 480–487).
+ San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.
-.. [4] Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. The MIT Press.
+.. [4] Koller, D., & Friedman, N. (2009).
+ Probabilistic graphical models: principles and techniques. The MIT Press.
"""