Generalized Bayes

Generalized Bayes methods extend traditional Bayesian inference by relaxing assumptions about the likelihood function, focusing instead on minimizing a cost function that measures the discrepancy between model predictions and observed data. Current research explores efficient algorithms, such as greedy approaches and those leveraging neural networks for amortized cost estimation, to improve the scalability and robustness of these methods, particularly in high-dimensional or computationally intensive settings like simulation-based inference. This broader framework enhances the applicability of Bayesian methods to complex problems with misspecified models or heavy-tailed data, leading to more robust and accurate inferences across diverse scientific domains.