Probabilistic Programming

Probabilistic programming (PP) is a paradigm that represents statistical models as computer programs, enabling automated inference and facilitating the development of complex probabilistic models. Current research emphasizes improving inference efficiency through advanced algorithms like variational inference and Markov Chain Monte Carlo (MCMC), often incorporating neural networks and exploring novel model architectures such as diffusion models. This approach enhances the expressiveness and scalability of probabilistic modeling, impacting diverse fields from Bayesian deep learning and robotics to causal inference and scientific modeling by providing a unified framework for representing and reasoning under uncertainty.