Learning Likelihoods With Conditional Normalizing Flows
In the field of machine learning and probabilistic modeling, understanding complex data distributions is a fundamental challenge. Traditional methods for modeling probability densities often struggle with high-dimensional or structured data, where simple assumptions like Gaussian distributions fall short. Conditional normalizing flows have emerged as a powerful tool for learning likelihoods and modeling complex conditional distributions. … Read more