Bayesian Model Selection

Bayesian model selection aims to identify the most probable model from a set of candidate models given observed data, balancing model complexity with its ability to explain the data. Current research focuses on improving the efficiency and scalability of Bayesian model selection, particularly for high-dimensional data and complex models like neural networks and those used in equation learning, often employing techniques like variational inference, Markov Chain Monte Carlo methods, and novel information criteria. These advancements are crucial for various fields, enabling more robust and reliable model building in applications ranging from biomedical interaction prediction to the discovery of governing equations in physical systems.