The field of Bayesian modeling and nonlinear filtering is witnessing significant developments, with a focus on improving the efficiency and accuracy of posterior inference and filtering methods. Researchers are exploring new approaches to tackle challenging problems, such as high-dimensional nonlinear filtering and conditional generative modeling. Notably, the use of diffusion-based methods and conditional reparameterization techniques is gaining traction. These innovations have the potential to enhance the performance of various applications, including image reconstruction and nonlinear system modeling. Noteworthy papers in this area include:

• Diffusion Bridge Variational Inference for Deep Gaussian Processes, which proposes a novel method for posterior inference in deep Gaussian processes.
• CAR-Flow: Condition-Aware Reparameterization Aligns Source and Target for Better Flow Matching, which introduces a lightweight, learned shift to improve flow matching in conditional generative modeling.
• An Efficient Conditional Score-based Filter for High Dimensional Nonlinear Filtering Problems, which presents a novel algorithm for efficient and accurate posterior sampling in high-dimensional nonlinear filtering problems.