Elliptical belief propagation

Generalized least generalized squares

We can generalize Gaussian belief propagation to use general elliptical laws by using Mahalanobis distance without presuming the Gaussian distribution (Agarwal et al. 2013; Davison and Ortiz 2019), making it into a kind of elliptical belief propagation.


If we use a robust Huber loss instead of a Gaussian log-likelihood, then the resulting algorithm is usually referred to as a robust factor or as dynamic covariance scaling (Agarwal et al. 2013; Davison and Ortiz 2019). The nice thing here is that we can imagine the transition from quadratic to linear losses gives us an estimate of which observations are outliers.


Surely this is around? Certainly there is a special case in the t-process. It is mentioned, I think, in Lan et al. (2006) and possibly Proudler et al. (2007) although the latter seems to be something more ad hoc.

Gaussian mixture

Surely? TBD.


There seem to be generic update rules (Aste 2021; Bånkestad et al. 2020) which could be used to construct a generic elliptical belief propagation algorithm.


Agarwal, Pratik, Gian Diego Tipaldi, Luciano Spinello, Cyrill Stachniss, and Wolfram Burgard. 2013. Robust Map Optimization Using Dynamic Covariance Scaling.” In 2013 IEEE International Conference on Robotics and Automation, 62–69.
Aste, Tomaso. 2021. Stress Testing and Systemic Risk Measures Using Multivariate Conditional Probability.” arXiv.
Bånkestad, Maria, Jens Sjölund, Jalil Taghia, and Thomas Schön. 2020. The Elliptical Processes: A Family of Fat-Tailed Stochastic Processes.” arXiv.
Cambanis, Stamatis, Steel Huang, and Gordon Simons. 1981. On the Theory of Elliptically Contoured Distributions.” Journal of Multivariate Analysis 11 (3): 368–85.
Chamberlain, Gary. 1983. A Characterization of the Distributions That Imply Mean—Variance Utility Functions.” Journal of Economic Theory 29 (1): 185–201.
Davison, Andrew J., and Joseph Ortiz. 2019. FutureMapping 2: Gaussian Belief Propagation for Spatial AI.” arXiv:1910.14139 [Cs], October.
Donoho, David L., and Andrea Montanari. 2013. High Dimensional Robust M-Estimation: Asymptotic Variance via Approximate Message Passing.” arXiv:1310.7320 [Cs, Math, Stat], October.
Karlgaard, Christopher D., and Hanspeter Schaub. 2011. Adaptive Nonlinear Huber-Based Navigation for Rendezvous in Elliptical Orbit.” Journal of Guidance, Control, and Dynamics 34 (2): 388–402.
Lan, Xiangyang, Stefan Roth, Daniel Huttenlocher, and Michael J. Black. 2006. Efficient Belief Propagation with Learned Higher-Order Markov Random Fields.” In Computer Vision – ECCV 2006, edited by Aleš Leonardis, Horst Bischof, and Axel Pinz, 3952:269–82. Berlin, Heidelberg: Springer Berlin Heidelberg.
Landsman, Zinoviy, and Johanna Nešlehová. 2008. Stein’s Lemma for Elliptical Random Vectors.” Journal of Multivariate Analysis 99 (5): 912–27.
Ortiz, Joseph, Talfan Evans, and Andrew J. Davison. 2021. A Visual Introduction to Gaussian Belief Propagation.” arXiv:2107.02308 [Cs], July.
Proudler, I., S. Roberts, S. Reece, and I. Rezek. 2007. An Iterative Signal Detection Algorithm Based on Bayesian Belief Propagation Ideas.” In 2007 15th International Conference on Digital Signal Processing, 355–58.

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