Complex Posterior

Complex posterior distributions pose a significant challenge in Bayesian inference, hindering accurate estimation of model parameters and latent variables. Current research focuses on developing efficient approximation methods, including variational inference techniques like amortized variational inference and novel approaches such as Bernstein flows and variational sequential Monte Carlo, often incorporating neural networks for flexible modeling of high-dimensional posteriors. These advancements aim to improve the scalability and accuracy of Bayesian methods across diverse applications, from time series analysis and causal inference to complex generative modeling and simulation-based inference. The resulting improvements in computational efficiency and approximation accuracy are crucial for advancing numerous fields relying on Bayesian statistical modeling.