Simulation Based Inference

Simulation-based inference (SBI) tackles Bayesian inference problems where the likelihood function is intractable, relying instead on simulations to learn the relationship between model parameters and observed data. Current research emphasizes efficient algorithms and neural network architectures, such as normalizing flows, Bayesian neural networks, and diffusion models, to approximate posterior distributions and accelerate inference, particularly in high-dimensional settings. This approach is proving valuable across diverse scientific fields, enabling robust parameter estimation and uncertainty quantification for complex models in areas ranging from cosmology and climate science to neuroscience and power systems optimization. The development of methods to address model misspecification and improve the scalability and reliability of SBI remains a key focus.