688 citations · 1.4k across the 6 of their papers we have counts for
6 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…
Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models
Louis Sharrock, Jack Simons, Song Liu +1
We introduce Sequential Neural Posterior Score Estimation (SNPSE), a score-based method for Bayesian inference in simulator-based models. Our method, inspired by the remarkable suc…
Overview of Approximate Bayesian Computation
S. A. Sisson, Y. Fan, M. A. Beaumont
This Chapter, "Overview of Approximate Bayesian Computation", is to appear as the first chapter in the forthcoming Handbook of Approximate Bayesian Computation (2018). It details t…
A note on the ABC-PRC algorithm of Sissons et al. (2007)
Mark A. Beaumont
This note describes the results of some tests of the ABC-PRC algorithm of Sissons et al. (PNAS, 2007), and demonstrates with a toy example that the method does not converge on the…
Adaptive approximate Bayesian computation
Mark A. Beaumont, Jean-Marie Cornuet, Jean-Michel Marin +1
Sequential techniques can enhance the efficiency of the approximate Bayesian computation algorithm, as in Sisson et al.'s (2007) partial rejection control version. While this metho…
Inferring population history with DIYABC: a user-friendly approach to Approximate Bayesian Computation
Jean-Marie Cornuet, Filipe Santos, Mark A. Beaumont +5
Genetic data obtained on population samples convey information about their evolutionary history. Inference methods can extract this information (at least partially) but they requir…