Ancestral Sampling

Ancestral sampling is a computational technique used to generate samples from complex probability distributions, particularly those arising in dynamic systems and generative models. Current research focuses on improving the efficiency and accuracy of ancestral sampling within various architectures, including diffusion models and variational autoencoders, often addressing challenges like mode collapse and slow convergence through novel algorithms and the incorporation of multiple data views. These advancements are impacting diverse fields, enabling improved trajectory reconstruction in areas like single-cell biology and realistic 3D motion generation from 2D video data, as well as enhancing causal inference techniques by leveraging ancestral information to address confounding variables.