Log Determinant

Log-determinant calculations are crucial in various fields, particularly for efficiently computing probabilities and likelihoods in probabilistic models and machine learning algorithms. Current research focuses on developing faster and more efficient algorithms for computing log-determinants, especially in high-dimensional settings, with applications ranging from Gaussian process regression to learning directed acyclic graphs (DAGs) and solving compressive sensing problems. These advancements are vital for improving the scalability and performance of numerous machine learning models and statistical inference methods, impacting fields like computer vision, quantum chemistry, and beyond.