activity
20172022
most citedNested sampling for physical scientists

158 citations · 171 across the 4 of their papers we have counts for

collaborators

5 papers

stat.CO2022★ 158 cited

Nested sampling for physical scientists

Greg Ashton, Noam Bernstein, Johannes Buchner +20

We review Skilling's nested sampling (NS) algorithm for Bayesian inference and more broadly multi-dimensional integration. After recapitulating the principles of NS, we survey deve…

stat.CO2019

Bayesian posterior repartitioning for nested sampling

Xi Chen, Farhan Feroz, Michael Hobson

Priors in Bayesian analyses often encode informative domain knowledge that can be useful in making the inference process more efficient. Occasionally, however, priors may be unrepr…

cs.LG2019★ 3 cited

Bayesian surrogate learning in dynamic simulator-based regression problems

Xi Chen, Mike Hobson

The estimation of unknown values of parameters (or hidden variables, control variables) that characterise a physical system often relies on the comparison of measured data with syn…

stat.CO2018

Improving the efficiency and robustness of nested sampling using posterior repartitioning

Xi Chen, Mike Hobson, Saptarshi Das +1

In real-world Bayesian inference applications, prior assumptions regarding the parameters of interest may be unrepresentative of their actual values for a given dataset. In particu…

physics.geo-ph2017★ 10 cited

Fast GPU-Based Seismogram Simulation from Microseismic Events in Marine Environments Using Heterogeneous Velocity Models

Saptarshi Das, Xi Chen, Michael P. Hobson

A novel approach is presented for fast generation of synthetic seismograms due to microseismic events, using heterogeneous marine velocity models. The partial differential equation…