Simulation-based methods for statistical inference have evolved dramatically over the past
50 years, kee** pace with technological advancements. The field is undergoing a new …

While decision theories have evolved over the past five decades, their focus has largely
been on choices among a limited number of discrete options, even though many real-world …

Recent advances in probabilistic modelling have led to a large number of simulation-based
inference algorithms which do not require numerical evaluation of likelihoods. However, a …

Simulation-based inference (SBI) solves statistical inverse problems by repeatedly running a
stochastic simulator and inferring posterior distributions from model-simulations. To improve …

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

Modern Bayesian inference involves a mixture of computational techniques for estimating,
validating, and drawing conclusions from probabilistic models as part of principled …

Neural posterior estimation methods based on discrete normalizing flows have become
established tools for simulation-based inference (SBI), but scaling them to high-dimensional …

This article proposes a new framework using physics-informed neural networks (PINNs) to
simulate complex structural systems that consist of single and double beams based on Euler …

Inferring parameters of computational models that capture experimental data is a central
task in cognitive neuroscience. Bayesian statistical inference methods usually require the …

Since their initial introduction, score-based diffusion models (SDMs) have been successfully
applied to solve a variety of linear inverse problems in finite-dimensional vector spaces due …