Factor Graph

Factor graphs are probabilistic graphical models used to represent complex systems by factorizing a joint probability distribution into smaller, more manageable components. Current research focuses on applying factor graphs to diverse problems, including robotics (e.g., simultaneous localization and mapping, multi-robot coordination), signal processing (e.g., channel estimation, code design), and machine learning (e.g., deep learning integration, causal inference), often employing algorithms like belief propagation and Gaussian process regression for inference and optimization. The flexibility and efficiency of factor graphs make them a powerful tool for integrating heterogeneous data sources and solving challenging inference problems across various scientific and engineering domains.