85 citations · 154 across the 11 of their papers we have counts for
12 papers
Causal Learning via Manifold Regularization
Steven M. Hill, Chris. J. Oates, Duncan A. Blythe +1
This paper frames causal structure estimation as a machine learning task. The idea is to treat indicators of causal relationships between variables as `labels' and to exploit avail…
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…
Towards a Multi-Subject Analysis of Neural Connectivity
Chris J. Oates, Lilia Carneiro da Costa, Tom Nichols
Directed acyclic graphs (DAGs) and associated probability models are widely used to model neural connectivity and communication channels. In many experiments, data are collected fr…
Exact Estimation of Multiple Directed Acyclic Graphs
Chris J. Oates, Jim Q. Smith, Sach Mukherjee +1
This paper considers the problem of estimating the structure of multiple related directed acyclic graph (DAG) models. Building on recent developments in exact estimation of DAGs us…
Quantifying the Multi-Scale Performance of Network Inference Algorithms
Chris J. Oates, Richard Amos, Simon E. F. Spencer
Graphical models are widely used to study complex multivariate biological systems. Network inference algorithms aim to reverse-engineer such models from noisy experimental data. It…
Joint Structure Learning of Multiple Non-Exchangeable Networks
Chris J. Oates, Sach Mukherjee
Several methods have recently been developed for joint structure learning of multiple (related) graphical models or networks. These methods treat individual networks as exchangeabl…
Network Inference Using Steady State Data and Goldbeter-Koshland Kinetics
Chris J Oates, Bryan T Hennessy, Yiling Lu +2
Network inference approaches are widely used to shed light on regulatory interplay between molecular players such as genes and proteins. Biochemical processes underlying networks o…
Joint estimation of multiple related biological networks
Chris J. Oates, Jim Korkola, Joe W. Gray +1
Graphical models are widely used to make inferences concerning interplay in multivariate systems. In many applications, data are collected from multiple related but nonidentical un…
On the relationship between ODEs and DBNs
Chris. J. Oates, Steven. M. Hill, Sach Mukherjee
Recently, Li et al. (Bioinformatics 27(19), 2686-91, 2011) proposed a method, called Differential Equation-based Local Dynamic Bayesian Network (DELDBN), for reverse engineering ge…
Network inference and biological dynamics
Chris. J. Oates, Sach Mukherjee
Network inference approaches are now widely used in biological applications to probe regulatory relationships between molecular components such as genes or proteins. Many methods h…