Statistical inference of simulator-based models used in many domains of science and engineering is challenging due to the unavailability of the likelihood function, which is the probability density function of the data given the parameters. To solve this problem, the field of simulation-based inference (SBI) has emerged, wherein large numbers of simulations from the model are utilised for inference instead of the likelihood function. However, the performance of state-of-the-art SBI methods severely degrades when the model fails to capture the real-world phenomenon under study (i.e. under model misspecification), or when the model is computationally costly to run, thus limiting the number of available simulations. DREAM4SBI aims to tackle these bottleneck challenges by providing solutions that are compute-efficient and robust to model misspecification, thus making SBI methods more widely applicable.

2024

2028