Particle belief propagation

Graphical inference using empirical distribution estimates



Empirical CDFs as approximate belief propagation updates.

References

Grooms, Ian, and Gregor Robinson. 2021. β€œA Hybrid Particle-Ensemble Kalman Filter for Problems with Medium Nonlinearity.” PLOS ONE 16 (3): e0248266.
Naesseth, Christian Andersson, Fredrik Lindsten, and Thomas B SchΓΆn. 2014. β€œSequential Monte Carlo for Graphical Models.” In Advances in Neural Information Processing Systems. Vol. 27. Curran Associates, Inc.
Naesseth, Christian, Fredrik Lindsten, and Thomas Schon. 2015. β€œNested Sequential Monte Carlo Methods.” In Proceedings of the 32nd International Conference on Machine Learning, 1292–1301. PMLR.
Paige, Brooks, and Frank Wood. 2016. β€œInference Networks for Sequential Monte Carlo in Graphical Models.” In Proceedings of The 33rd International Conference on Machine Learning, 3040–49. PMLR.

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