Lifted Message Passing for Hybrid Probabilistic Inference
Authors: Yuqiao Chen, Nicholas Ruozzi, Sriraam Natarajan
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate empirically that our approximate lifting schemes perform comparably to existing state-of-the-art models for Gaussian MLNs, while having the flexibility to be applied to models with arbitrary potential functions. |
| Researcher Affiliation | Academia | Yuqiao Chen , Nicholas Ruozzi and Sriraam Natarajan University of Texas at Dallas {yuqiao.chen, nicholas.ruozzi, sriraam.natarajan}@utdallas.edu |
| Pseudocode | Yes | Algorithm 1 Lifted Hybrid EPBP |
| Open Source Code | Yes | EPBP, LEPBP, C2FEPBP, and LGa BP were implemented in Python 3.6, and all source code is available on Git Hub1. 1Code: github.com/leodd/Hybrid-Lifted-Belief-Propagation |
| Open Datasets | Yes | We used groundwater level data extracted from the Republican River Compact Association model [Mc Kusick, 2003], which is a monthly record of the measured head position of 3420 wells over 850 months. |
| Dataset Splits | No | The paper describes experiments on probabilistic inference models using evidence and observations, but it does not specify explicit training or validation dataset splits typically used in supervised learning contexts. |
| Hardware Specification | Yes | All experiments were performed on a machine with a 2.2 GHz Intel Core i7-8750H CPU and 16 GB of memory. |
| Software Dependencies | Yes | EPBP, LEPBP, C2FEPBP, and LGa BP were implemented in Python 3.6 |
| Experiment Setup | Yes | All message-passing algorithms were run for 15 iterations and sampling-based methods used 20 sampling points for the integral approximations. For coarse-to-fine lifting, we use k-means clustering with k = 2 for evidence group splitting and use dynamic splitting of the threshold which was initially being set to ϵ = max Sa S(avg(v Sa)) and was decreased each iteration until ϵ = 0. |