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 …

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 …

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

Agent-based models (ABMs) are an increasingly popular choice for simulating large
systems of interacting components, and have been applied across a wide variety of natural …

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 …

Response speeds in simple decision-making tasks begin to decline from early and middle
adulthood. However, response times are not pure measures of mental speed but instead …