14 citations · 44 across the 9 of their papers we have counts for
9 papers
Discussion of: "A Bayesian information criterion for singular models"
N. Friel, J. P. McKeone, C. J. Oates +1
Contributed discussion to the paper of Drton and Plummer (2017), presented before the Royal Statistical Society on 5th October 2016.
Discussion of "Sequential Quasi-Monte Carlo" by Mathieu Gerber and Nicolas Chopin
Chris. J. Oates, Daniel Simpson, Mark Girolami
A discussion on the possibility of reducing the variance of quasi-Monte Carlo estimators in applications. Further details are provided in the accompanying paper "Variance Reduction…
Estimating causal structure using conditional DAG models
Chris J. Oates, Jim Q. Smith, Sach Mukherjee
This paper considers inference of causal structure in a class of graphical models called "conditional DAGs". These are directed acyclic graph (DAG) models with two kinds of variabl…
Exploiting Multi-Core Architectures for Reduced-Variance Estimation with Intractable Likelihoods
Nial Friel, Antonietta Mira, Chris. J. Oates
Many popular statistical models for complex phenomena are intractable, in the sense that the likelihood function cannot easily be evaluated. Bayesian estimation in this setting rem…
Causal network inference using biochemical kinetics
C. J. Oates, F. Dondelinger, N. Bayani +3
Network models are widely used as structural summaries of biochemical systems. Statistical estimation of networks is usually based on linear or discrete models. However, the dynami…
The Controlled Thermodynamic Integral for Bayesian Model Comparison
Chris J. Oates, Theodore Papamarkou, Mark Girolami
Bayesian model comparison relies upon the model evidence, yet for many models of interest the model evidence is unavailable in closed form and must be approximated. Many of the est…