26 citations · 62 across the 6 of their papers we have counts for
7 papers
Efficient structure learning with automatic sparsity selection for causal graph processes
Théophile Griveau-Billion, Ben Calderhead
We propose a novel algorithm for efficiently computing a sparse directed adjacency matrix from a group of time series following a causal graph process. Our solution is scalable for…
A Dynamic Bayesian Model for Interpretable Decompositions of Market Behaviour
Théophile Griveau-Billion, Ben Calderhead
We propose a heterogeneous simultaneous graphical dynamic linear model (H-SGDLM), which extends the standard SGDLM framework to incorporate a heterogeneous autoregressive realised…
Quasi Markov Chain Monte Carlo Methods
Tobias Schwedes, Ben Calderhead
Quasi-Monte Carlo (QMC) methods for estimating integrals are attractive since the resulting estimators typically converge at a faster rate than pseudo-random Monte Carlo. However,…
Implicit Probabilistic Integrators for ODEs
Onur Teymur, Han Cheng Lie, Tim Sullivan +1
We introduce a family of implicit probabilistic integrators for initial value problems (IVPs), taking as a starting point the multistep Adams-Moulton method. The implicit construct…
Probabilistic Linear Multistep Methods
Onur Teymur, Konstantinos Zygalakis, Ben Calderhead
We present a derivation and theoretical investigation of the Adams-Bashforth and Adams-Moulton family of linear multistep methods for solving ordinary differential equations, start…
Bayesian Solution Uncertainty Quantification for Differential Equations
Oksana A. Chkrebtii, David A. Campbell, Ben Calderhead +1
We explore probability modelling of discretization uncertainty for system states defined implicitly by ordinary or partial differential equations. Accounting for this uncertainty c…
Riemannian Manifold Hamiltonian Monte Carlo
Mark Girolami, Ben Calderhead, Siu A. Chin
The paper proposes a Riemannian Manifold Hamiltonian Monte Carlo sampler to resolve the shortcomings of existing Monte Carlo algorithms when sampling from target densities that may…