Imperial College London

DrBenCalderhead

Faculty of Natural SciencesDepartment of Mathematics

Lecturer in Statistics
 
 
 
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Contact

 

+44 (0)20 7594 8577b.calderhead

 
 
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Location

 

523Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

20 results found

Teymur O, Calderhead B, Lie HC, Sullivan TJet al., 2018, Implicit Probabilistic Integrators for ODEs, 32nd Conference on Neural Information Processing Systems (NIPS), Publisher: NEURAL INFORMATION PROCESSING SYSTEMS (NIPS), ISSN: 1049-5258

CONFERENCE PAPER

Chkrebtii OA, Campbell DA, Calderhead B, Girolami MAet al., 2016, o Rejoinder, BAYESIAN ANALYSIS, Vol: 11, Pages: 1295-1299, ISSN: 1931-6690

JOURNAL ARTICLE

Chkrebtii OA, Campbell DA, Calderhead B, Girolami MAet al., 2016, Bayesian Solution Uncertainty Quantification for Differential Equations, BAYESIAN ANALYSIS, Vol: 11, Pages: 1239-1267, ISSN: 1931-6690

JOURNAL ARTICLE

Epstein M, Calderhead B, Girolami MA, Sivilotti LGet al., 2016, Bayesian Statistical Inference in Ion-Channel Models with Exact Missed Event Correction, BIOPHYSICAL JOURNAL, Vol: 111, Pages: 333-348, ISSN: 0006-3495

JOURNAL ARTICLE

Groen D, Calderhead B, 2015, Science hackathons for developing interdisciplinary research and collaborations, ELIFE, Vol: 4, ISSN: 2050-084X

JOURNAL ARTICLE

Knapp B, Bardenet R, Bernabeu MO, Bordas R, Bruna M, Calderhead B, Cooper J, Fletcher AG, Groen D, Kuijper B, Lewis J, McInerny G, Minssen T, Osborne J, Paulitschke V, Pitt-Francis J, Todoric J, Yates CA, Gavaghan D, Deane CMet al., 2015, Ten Simple Rules for a Successful Cross-Disciplinary Collaboration, PLOS COMPUTATIONAL BIOLOGY, Vol: 11, ISSN: 1553-734X

JOURNAL ARTICLE

Calderhead B, 2014, A general construction for parallelizing Metropolis-Hastings algorithms, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 111, Pages: 17408-17413, ISSN: 0027-8424

JOURNAL ARTICLE

Smith MJ, Palmer PI, Purves DW, Vanderwel MC, Lyutsarev V, Calderhead B, Joppa LN, Bishop CM, Emmott Set al., 2014, CHANGING HOW EARTH SYSTEM MODELING IS DONE TO PROVIDE MORE USEFUL INFORMATION FOR DECISION MAKING, SCIENCE, AND SOCIETY, BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, Vol: 95, Pages: 1453-1464, ISSN: 0003-0007

JOURNAL ARTICLE

Kramer A, Calderhead B, Radde N, 2014, Hamiltonian Monte Carlo methods for efficient parameter estimation in steady state dynamical systems, BMC BIOINFORMATICS, Vol: 15, ISSN: 1471-2105

JOURNAL ARTICLE

Osborne JM, Bernabeu MO, Bruna M, Calderhead B, Cooper J, Dalchau N, Dunn S-J, Fletcher AG, Freeman R, Groen D, Knapp B, McInerny GJ, Mirams GR, Pitt-Francis J, Sengupta B, Wright DW, Yates CA, Gavaghan DJ, Emmott S, Deane Cet al., 2014, Ten Simple Rules for Effective Computational Research, PLOS COMPUTATIONAL BIOLOGY, Vol: 10, ISSN: 1553-734X

JOURNAL ARTICLE

Wang Y, Huang K, Cao Z, Standing JR, Calderhead Bet al., 2014, Bayesian identification of soil strata in London Clay, GEOTECHNIQUE, Vol: 64, Pages: 1014-1016, ISSN: 0016-8505

JOURNAL ARTICLE

Calderhead B, Sustik M, 2013, Sparse Approximate Manifolds for Differential Geometric MCMC, 26th Annual Conference on Neural Information Processing Systems 2012, Publisher: Curran Associates Inc., Pages: 2879-2887

CONFERENCE PAPER

Calderhead B, Epstein M, Sivilotti L, Girolami Met al., 2013, Bayesian approaches for mechanistic ion channel modeling., Methods Mol Biol, Vol: 1021, Pages: 247-272

We consider the Bayesian analysis of mechanistic models describing the dynamic behavior of ligand-gated ion channels. The opening of the transmembrane pore in an ion channel is brought about by conformational changes in the protein, which results in a flow of ions through the pore. Remarkably, given the diameter of the pore, the flow of ions from a small number of channels or indeed from a single ion channel molecule can be recorded experimentally. This produces a large time-series of high-resolution experimental data, which can be used to investigate the gating process of these channels. We give a brief overview of the achievements and limitations of alternative maximum-likelihood approaches to this type of modeling, before investigating the statistical issues associated with analyzing stochastic model reaction mechanisms from a Bayesian perspective. Finally, we compare a number of Markov chain Monte Carlo algorithms that may be used to tackle this challenging inference problem.

JOURNAL ARTICLE

Mohamed L, Calderhead B, Filippone M, Christie M, Girolami Met al., 2012, Population MCMC methods for history matching and uncertainty quantification, 12th European Conference on the Mathematics of Oil Recovery (ECMOR), Publisher: SPRINGER, Pages: 423-436, ISSN: 1420-0597

CONFERENCE PAPER

Calderhead B, Girolami M, 2011, Statistical analysis of nonlinear dynamical systems using differential geometric sampling methods, INTERFACE FOCUS, Vol: 1, Pages: 821-835, ISSN: 2042-8898

JOURNAL ARTICLE

Girolami M, Calderhead B, 2011, Riemann manifold Langevin and Hamiltonian Monte Carlo methods, JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, Vol: 73, Pages: 123-214, ISSN: 1369-7412

JOURNAL ARTICLE

Girolami M, Calderhead B, Vyshemirsky V, 2009, System Identification and Model Ranking: The Bayesian Perspective Learning and Inference, Learning and Inference in Computational Systems Biology, Editors: Lawrence, Girolami, Rattray, Sanguinetti, ISBN: 9780262013864

BOOK CHAPTER

Calderhead B, Girolami M, 2009, Estimating Bayes factors via thermodynamic integration and population MCMC, COMPUTATIONAL STATISTICS & DATA ANALYSIS, Vol: 53, Pages: 4028-4045, ISSN: 0167-9473

JOURNAL ARTICLE

Calderhead B, Girolami M, Lawrence N, 2009, Accelerating Bayesian inference over nonlinear differential equations with Gaussian processes

CONFERENCE PAPER

Luo X, Calderhead B, Liu H, Li Wet al., 2007, On the initial configurations of collapsible channel flow, 4th MIT Conference on Computational Fluid and Solid Mechanics, Publisher: PERGAMON-ELSEVIER SCIENCE LTD, Pages: 977-987, ISSN: 0045-7949

CONFERENCE PAPER

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