B. Calderhead

发表

Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes

Neil D. Lawrence, Mark A. Girolami, Ben Calderhead, 2008, NIPS.

A study of Population MCMC for estimatingBayes Factors over nonlinear ODE models

B. Calderhead, 2008 .

ST ] 2 O ct 2 01 8 Quasi Markov Chain Monte Carlo Methods

B. Calderhead, Tobias Schwedes, 2018 .

Quasi Markov Chain Monte Carlo Methods

B. Calderhead, Tobias Schwedes, 2018, 1807.00070.

Bayesian Solution Uncertainty Quantification for Differential Equations

M. Girolami, O. Chkrebtii, B. Calderhead, 2013 .

Bayesian Uncertainty Quantication for Dierential Equations

Mark Girolami, David A. Campbell, Ben Calderhead, 2014 .

Riemann manifold Langevin and Hamiltonian Monte Carlo methods

M. Girolami, B. Calderhead, 2011, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Inference for Gaussian process emulation of oil reservoir simulation codes

Michael Andrew Christie, Maurizio Filippone, Mark Girolami, 2010 .

Advances in Neural Information Processing Systems 29

Ben Calderhead, Onur Teymur, Kostas Zygalakis, 2016 .

A Dynamic Bayesian Model for Interpretable Decompositions of Market Behaviour

B. Calderhead, T. Griveau-Billion, Théophile Griveau-Billion, 2019, 1904.08153.

Bayesian Uncertainty Quantification for Differential Equations

Mark Girolami, David A. Campbell, Ben Calderhead, 2013 .

Estimating Bayes factors via thermodynamic integration and population MCMC

Mark A. Girolami, Ben Calderhead, M. Girolami, 2009, Comput. Stat. Data Anal..

Statistical analysis of nonlinear dynamical systems using differential geometric sampling methods

Mark Girolami, Ben Calderhead, M. Girolami, 2011, Interface Focus.

Differential geometric MCMC methods and applications

B. Calderhead, 2011 .

Implicit Probabilistic Integrators for ODEs

Ben Calderhead, Tim Sullivan, Han Cheng Lie, 2018, NeurIPS.

Probabilistic Linear Multistep Methods

Ben Calderhead, Konstantinos Zygalakis, Onur Teymur, 2016, NIPS.

Efficient computation of mean reverting portfolios using cyclical coordinate descent

Ben Calderhead, Th'eophile Griveau-Billion, B. Calderhead, 2019, 1905.05841.

Hamiltonian Monte Carlo methods for efficient parameter estimation in steady state dynamical systems

Nicole Radde, Ben Calderhead, Andrei Kramer, 2014, BMC Bioinformatics.

Population MCMC methods for history matching and uncertainty quantification

Michael Andrew Christie, Maurizio Filippone, Mark Girolami, 2010, Computational Geosciences.

Ten Simple Rules for Effective Computational Research

Alexander G. Fletcher, Charlotte M. Deane, David Gavaghan, 2014, PLoS Comput. Biol..

On the initial configurations of collapsible channel flow

B. Calderhead, Xiaoyu Luo, Wenguang Li, 2007 .

GLASSOFAST : An efficient GLASSO implementation

Mátyás A. Sustik, Ben Calderhead, B. Calderhead, 2012 .

Ten Simple Rules for a Successful Cross-Disciplinary Collaboration

Alexander G. Fletcher, Charlotte M. Deane, David Gavaghan, 2015, PLoS Comput. Biol..

Changing How Earth System Modeling is Done to Provide More Useful Information for Decision Making, Science, and Society

Vassily Lyutsarev, Stephen Emmott, Ben Calderhead, 2014 .

A general construction for parallelizing Metropolis−Hastings algorithms

Ben Calderhead, B. Calderhead, 2014, Proceedings of the National Academy of Sciences.

Riemannian Manifold Hamiltonian Monte Carlo

Ben Calderhead, Mark Girolami, Siu A. Chin, 2009, 0907.1100.

Science hackathons for developing interdisciplinary research and collaborations

Ben Calderhead, B. Calderhead, Derek Groen, 2015, eLife.

Ten Simple Rules for a Successful Cross-Disciplinary Collaboration

Christian A. Yates, C. Deane, R. Bardenet, 2015, PLoS Comput. Biol..

Ten Simple Rules for a Successful Cross- Disciplinary Collaboration

Christian A. Yates, C. Deane, R. Bardenet, 2015 .

Mathematics Of Planet Earth: A Primer

Darryl D. Holm, D. Crisan, T. Kuna, 2017 .

Bayesian Statistical Inference in Ion-Channel Models with Exact Missed Event Correction.

M. Girolami, B. Calderhead, L. Sivilotti, 2016, Biophysical Journal.

Bayesian Uncertainty Quantification for Differential Equations

M. Girolami, O. Chkrebtii, B. Calderhead, 2013, 1306.2365.

System Identification and Model Ranking: The Bayesian Perspective

Mark Girolami, Ben Calderhead, Vladislav Vyshemirsky, 2010, Learning and Inference in Computational Systems Biology.

Sparse Approximate Manifolds for Differential Geometric MCMC

Mátyás A. Sustik, Ben Calderhead, B. Calderhead, 2012, NIPS.

Bayesian approaches for mechanistic ion channel modeling.

Mark Girolami, Ben Calderhead, Michael Epstein, 2013, Methods in molecular biology.