We analyze belief propagation on patch potential models -- these are attractive models with varying local potentials -- obtain all of the possibly many fixed points, and gather novel insights into belief propagation's properties. In particular, we observe and theoretically explain several regions in the parameter space that behave fundamentally different. We specify and elaborate on one specific region that, despite the existence of multiple fixed points, is relatively well behaved and provides insights into the relationship between the accuracy of the marginals and the partition function. We demonstrate the inexistence of a principle relationship between both quantities and provide sufficient conditions for a fixed point to be optimal with respect to approximating both the marginals and the partition function.
|Title of host publication||Uncertainty in Artificial Intelligence|
|Publication status||Published - 23 Jul 2019|
|Event||2019 Conference on Uncertainty in Artificial Intelligence - Tel Aviv, Israel|
Duration: 22 Jul 2019 → 25 Jul 2019
|Conference||2019 Conference on Uncertainty in Artificial Intelligence|
|Abbreviated title||UAI 2019|
|Period||22/07/19 → 25/07/19|