Approximate Inference

Approximate inference tackles the computational challenge of estimating probability distributions in complex models, aiming to provide efficient and accurate approximations of intractable posterior distributions. Current research focuses on developing novel algorithms, such as variational inference, expectation propagation, and particle-based methods, often within the context of Bayesian neural networks, Gaussian processes, and probabilistic graphical models. These advancements are crucial for scaling Bayesian methods to large datasets and complex problems in machine learning, enabling more robust and reliable predictions with associated uncertainty quantification in diverse applications like robotics, healthcare, and scientific modeling. Furthermore, research emphasizes improving the efficiency and accuracy of these approximations, particularly in resource-constrained settings and under distribution shifts.