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Explore structure learning for probabilistic graphical models through the Benchpress workflow in this 58-minute tutorial from the Uncertainty in Artificial Intelligence conference. Gain insights into the computational challenges of learning graphical structures and causal discovery. Discover how Benchpress facilitates access to various structure learning algorithms through a scalable, platform-independent Snakemake workflow. Learn to use Benchpress's simple JSON interface to run, develop, and create reproducible benchmarks. Understand how Benchpress interfaces with state-of-the-art algorithms from libraries like BDgraph, BiDAG, bnlearn, and more. Explore its capabilities for data generation, performance evaluation, and inclusion of standard datasets and graphical models. Follow along as the presenters demonstrate practical applications of Benchpress, equipping you with valuable skills for structure learning in probabilistic graphical models.
