Efficient Bayesian Inference

Efficient Bayesian inference aims to overcome the computational challenges of probabilistic modeling by developing faster and more scalable algorithms for estimating posterior distributions. Current research focuses on leveraging probabilistic circuits, variational Bayesian Monte Carlo methods, and neural network architectures (including Bayesian neural networks and Hamiltonian neural networks) to achieve this goal, often incorporating techniques like Laplace approximations and importance sampling. These advancements enable more efficient Bayesian inference in diverse applications, from complex scientific modeling and image generation to parameter-efficient fine-tuning of large language models, ultimately improving the accuracy and reliability of probabilistic predictions.