Probabilistic Program

Probabilistic programming aims to represent and reason with probabilistic models using the familiar syntax and semantics of computer programs, facilitating both model specification and inference. Current research emphasizes efficient inference algorithms, particularly variational inference and Monte Carlo methods, often tailored to specific challenges like handling stochastic support, high-dimensional integer distributions, and hybrid continuous-discrete models. This field is significant for its potential to improve the scalability and reliability of probabilistic inference across diverse applications, from machine learning and robotics to causal inference and program verification.