![]() Inference, such as making predictions or decisions, is perhaps the most fundamental issue for any graphical model. What technology problem will I help solve? Have contributors create new algorithms implementations or improve existing ones is paramount to achieving this goal. We expect this framework could be regarded as the gold standard in the artificial intelligence research community. The long-term objective of this project is to develop an open, easy-to-use, extensible framework to facilitate efficient exact and approximate probabilistic inference over graphical models. For MAP and Marginal MAP inference, Merlin employs advanced, search-based algorithms that exploit problem decomposition by traversing the AND/OR search space associated with graphical models. Merlin implements the classic Loopy Belief Propagation (LBP) algorithm as well as more advanced generalized belief propagation algorithms, such as Iterative Join-Graph Propagation (IJGP) and Weighted Mini-Bucket Elimination (WMB). It supports the most common inference tasks such as computing the partition function or probability of evidence (PR), posterior marginals (MAR), as well as finding MAP (maximum a posteriori or most probable explanation) and Marginal MAP configurations. Merlin is an extensible C++ library that implements state-of-the-art exact and approximate algorithms for inference over probabilistic graphical models. To tackle these problems in the future, we must accelerate the quest for more efficient, scalable inference algorithms capable of exploiting the underlying structure of the problem and of harnessing the computational power of the cloud. These models use graphs (directed or undirected) to capture the structure of extremely complex problems often involving hundreds or even many thousands of interacting variables.Ĭalculating the relevant probabilities (also known as inference) in a graphical model involves challenging computational problems of optimization and estimation in highly dimensional spaces and, therefore, becomes a practical issue in many situations. Probabilistic graphical models (or graphical models for short) allow systems and businesses to address these challenges in a unified framework. Many real-world problems in artificial intelligence, computer vision, robotics, computer systems, computational biology, and natural language processing require systems to reason about highly uncertain, structured or unstructured data, and draw global insights from local observations.
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