Marginal Inference in Continuous Markov Random Fields Using Mixtures
Authors: Yuanzhen Guo, Hao Xiong, Nicholas Ruozzi7834-7841
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide support for our claimed advantages from both a theoretical and a practical perspective: We apply our method to a variety of problems arising from real and synthetic data sets, in each case demonstrating the superior performance of our approach for the marginal inference task. |
| Researcher Affiliation | Academia | Yuanzhen Guo, Hao Xiong, Nicholas Ruozzi University of Texas at Dallas 800 W. Campbell Road Richardson, TX 75080 {yuanzhen.guo, hao.xiong, nicholas.ruozzi}@utdallas.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | For this set of experiments, we selected a variety of data sets from the UCI Machine Learning Repository (Dheeru and Karra Taniskidou 2017) with between 4 and 30 variables. |
| Dataset Splits | No | The paper describes model evaluation and approximation performance but does not specify explicit training, validation, and test dataset splits for model training in the traditional sense. It evaluates inference methods on pre-constructed graphical models. |
| Hardware Specification | Yes | We applied our method directly on the pixel level with L = 1 and KQ = 11 near the zero temperature limit with a GPU implementation on an NVIDIA Tesla V100. |
| Software Dependencies | No | We implemented our approach that approximates the beliefs as independent Gaussian mixtures, dubbed QBethe, using standard projected gradient ascent with a diminishing step size rule in MATLAB without parallelization. We compare against the Gaussian EP, EPBP, and PBP methods (also implemented in MATLAB). No specific version numbers for MATLAB or any other libraries are provided. |
| Experiment Setup | Yes | For these experiments, QBethe was run from a random intialization with KQ = 4 quadrature points and L = 5 mixture components. PBP and EPBP were run with 20 particles to ensure that all three methods have roughly the same per iteration complexity and use the same number of points in the integral approximations. [...] The number of particles for the sampling methods was set to 100. QBethe was run with L = 1 and three quadrature points. |