12 citations · 32 across the 6 of their papers we have counts for
8 papers
Robust Neural Posterior Estimation and Statistical Model Criticism
Daniel Ward, Patrick Cannon, Mark Beaumont +2
Computer simulations have proven a valuable tool for understanding complex phenomena across the sciences. However, the utility of simulators for modelling and forecasting purposes…
Investigating the Impact of Model Misspecification in Neural Simulation-based Inference
Patrick Cannon, Daniel Ward, Sebastian M. Schmon
Aided by advances in neural density estimation, considerable progress has been made in recent years towards a suite of simulation-based inference (SBI) methods capable of performin…
Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation
Joel Dyer, Patrick Cannon, Sebastian M Schmon
Simulation models of complex dynamics in the natural and social sciences commonly lack a tractable likelihood function, rendering traditional likelihood-based statistical inference…
Generalized Posteriors in Approximate Bayesian Computation
Sebastian M Schmon, Patrick W Cannon, Jeremias Knoblauch
Complex simulators have become a ubiquitous tool in many scientific disciplines, providing high-fidelity, implicit probabilistic models of natural and social phenomena. Unfortunate…
A General Framework for Survival Analysis and Multi-State Modelling
Stefan Groha, Sebastian M Schmon, Alexander Gusev
Survival models are a popular tool for the analysis of time to event data with applications in medicine, engineering, economics, and many more. Advances like the Cox proportional h…
Implicit Priors for Knowledge Sharing in Bayesian Neural Networks
Jack K Fitzsimons, Sebastian M Schmon, Stephen J Roberts
Bayesian interpretations of neural network have a long history, dating back to early work in the 1990's and have recently regained attention because of their desirable properties l…