Bayesian Tensor

Bayesian tensor methods aim to analyze and model high-dimensional data represented as tensors (multi-dimensional arrays) by incorporating prior knowledge and uncertainty quantification within a Bayesian framework. Current research emphasizes developing efficient algorithms for various tensor decompositions (e.g., Tucker, CP) and incorporating sparsity constraints to handle large, incomplete, or noisy datasets, often employing variational inference or expectation-maximization techniques. These advancements are impacting diverse fields, enabling improved data analysis in areas such as bioinformatics (integrating multi-omics data), image processing (completing missing data), and material science (representing complex material properties).