Probabilistic Inference in Hybrid Domains by Weighted Model Integration
Authors: Vaishak Belle, Andrea Passerini, Guy Van den Broeck
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An empirical evaluation demonstrates the applicability and promise of the proposal. ... In this section, we discuss results on an implementation of WMI inference and CHMN parameter learning. ... To test the scaling behavior, we are mainly concerned with the volume computation aspect of the WMI task. ... Next, we demonstrate parameter learning, and the diversity of applications that WMI can characterize, well beyond the standard hybrid examples. |
| Researcher Affiliation | Academia | Vaishak Belle Dept. of Computer Science KU Leuven, Belgium vaishak@cs.kuleuven.be Andrea Passerini DISI University of Trento, Italy passerini@disi.unitn.it Guy Van den Broeck Dept. of Computer Science KU Leuven, Belgium guy.vandenbroeck@cs.kuleuven.be |
| Pseudocode | No | The paper describes algorithms and concepts but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions implementing their system using Z3 SMT solver v4.3.2 and Latt E software v1.6 for computing integrals, which are third-party tools. It does not state that the authors are releasing their own code for the methodology described. |
| Open Datasets | Yes | It uses a data series released by the UK government that provides average journey time, speed and traffic flow information on all motorways, known as the Strategic Road Network, in England.10 ... In the following we consider the 2012 dataset, with over 7 million entries. |
| Dataset Splits | No | The paper mentions using the 2012 dataset with over 7 million entries for parameter learning but does not provide specific train/validation/test splits, percentages, or sample counts. |
| Hardware Specification | Yes | All experiments were run using a system with 1.7 GHz Intel Core i7 and 8GB RAM. |
| Software Dependencies | Yes | Our system is implemented using the Z3 SMT solver v4.3.2,8 and the Latt E software v1.6 for computing integrals.9 |
| Experiment Setup | Yes | The weights are initialized to 1, and Figure 2b plots how the weights diverge for the formulas f1 and f2 when likelihood estimation terminates. |