Conditional generative models became a very powerful tool to sample from Bayesian
inverse problem posteriors. It is well-known in classical Bayesian literature that posterior …

Simulation-based inference techniques are indispensable for parameter estimation of
mechanistic and simulable models with intractable likelihoods. While traditional statistical …

Bayesian inference is a powerful framework for making probabilistic inferences and
decisions under uncertainty. Fundamental choices in modern Bayesian workflows concern …

Scientific modeling applications often require estimating a distribution of parameters
consistent with a dataset of observations-an inference task also known as source distribution …

Computational models are invaluable in capturing the complexities of real-world biological
processes. Yet, the selection of appropriate algorithms for inference tasks, especially when …

Recent advances in probabilistic deep learning enable efficient amortized Bayesian
inference in settings where the likelihood function is only implicitly defined by a simulation …

Neural amortized Bayesian inference (ABI) can solve probabilistic inverse problems orders
of magnitude faster than classical methods. However, neural ABI is not yet sufficiently robust …

Simulation based inference (SBI) methods enable the estimation of posterior distributions
when the likelihood function is intractable, but where model simulation is feasible. Popular …

Scientists and engineers use simulators to model empirically observed phenomena.
However, tuning the parameters of a simulator to ensure its outputs match observed data …

Scientific discoveries are driven by data, either collected through controlled experiments or
by observing the world. Those scientific theories can take the form of a mathematical model …