Simulation-based inference (SBI) methods such as approximate Bayesian computation
(ABC), synthetic likelihood, and neural posterior estimation (NPE) rely on simulating …

We propose a novel approach for estimating conditional or parametric expectations in the
setting where obtaining samples or evaluating integrands is costly. Through the framework …

Abstract Multilevel Monte Carlo is a key tool for approximating integrals involving expensive
scientific models. The idea is to use approximations of the integrand to construct an …

Model misspecification can create significant challenges for the implementation of
probabilistic models, and this has led to development of a range of robust methods which …

Generative models are invaluable in many fields of science because of their ability to
capture high-dimensional and complicated distributions, such as photo-realistic images …

Generative models are invaluable in many fields of science because of their ability to
capture high-dimensional and complicated distributions, such as photo-realistic images …

Intractable generative models, or simulators, are models for which the likelihood is
unavailable but sampling is possible. Most approaches to parameter inference in this setting …

Federated learning (FL) is a promising machine learning paradigm that collaborates with
client models to capture global knowledge. However, deploying FL models in real-world …

Stochastic simulation models are generative models that mimic complex systems to help
with decision-making. The reliability of these models heavily depends on well-calibrated …

Simulation-based inference (SBI) is the preferred framework for estimating parameters of
intractable models in science and engineering. A significant challenge in this context is the …