Imperial College London

ProfessorMarkGirolami

Faculty of Natural SciencesDepartment of Mathematics

Chair in Statistics
 
 
 
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Contact

 

m.girolami Website

 
 
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Location

 

539Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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15 results found

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

Girolami MA, 2014, Big Bayes Stories: A Collection of Vignettes, STATISTICAL SCIENCE, Vol: 29, Pages: 97-97, ISSN: 0883-4237

JOURNAL ARTICLE

Jiwaji M, Sandison ME, Reboud J, Stevenson R, Daly R, Barkess G, Faulds K, Kolch W, Graham D, Girolami MA, Cooper JM, Pitt ARet al., 2014, Quantification of Functionalised Gold Nanoparticle-Targeted Knockdown of Gene Expression in HeLa Cells, PLOS ONE, Vol: 9, ISSN: 1932-6203

JOURNAL ARTICLE

Stathopoulos V, Girolami MA, 2013, Markov chain Monte Carlo inference for Markov jump processes via the linear noise approximation, PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, Vol: 371, ISSN: 1364-503X

JOURNAL ARTICLE

Stathopoulos V, Girolami MA, 2013, Markov chain Monte Carlo inference for Markov jump processes via the linear noise approximation., Philos Trans A Math Phys Eng Sci, Vol: 371, ISSN: 1364-503X

Bayesian analysis for Markov jump processes (MJPs) is a non-trivial and challenging problem. Although exact inference is theoretically possible, it is computationally demanding, thus its applicability is limited to a small class of problems. In this paper, we describe the application of Riemann manifold Markov chain Monte Carlo (MCMC) methods using an approximation to the likelihood of the MJP that is valid when the system modelled is near its thermodynamic limit. The proposed approach is both statistically and computationally efficient whereas the convergence rate and mixing of the chains allow for fast MCMC inference. The methodology is evaluated using numerical simulations on two problems from chemical kinetics and one from systems biology.

JOURNAL ARTICLE

Jiwaji M, Daly R, Gibriel A, Barkess G, McLean P, Yang J, Pansare K, Cumming S, McLauchlan A, Kamola PJ, Bhutta MS, West AG, West KL, Kolch W, Girolami MA, Pitt ARet al., 2012, Unique Reporter-Based Sensor Platforms to Monitor Signalling in Cells, PLOS ONE, Vol: 7, ISSN: 1932-6203

JOURNAL ARTICLE

Good DM, Zuerbig P, Argiles A, Bauer HW, Behrens G, Coon JJ, Dakna M, Decramer S, Delles C, Dominiczak AF, Ehrich JHH, Eitner F, Fliser D, Frommberger M, Ganser A, Girolami MA, Golovko I, Gwinner W, Haubitz M, Herget-Rosenthal S, Jankowski J, Jahn H, Jerums G, Julian BA, Kellmann M, Kliem V, Kolch W, Krolewski AS, Luppi M, Massy Z, Melter M, Neusuess C, Novak J, Peter K, Rossing K, Rupprecht H, Schanstra JP, Schiffer E, Stolzenburg J-U, Tarnow L, Theodorescu D, Thongboonkerd V, Vanholder R, Weissinger EM, Mischak H, Schmitt-Kopplin Pet al., 2010, Naturally Occurring Human Urinary Peptides for Use in Diagnosis of Chronic Kidney Disease, MOLECULAR & CELLULAR PROTEOMICS, Vol: 9, Pages: 2424-2437, ISSN: 1535-9476

JOURNAL ARTICLE

Hopcroft LEM, McBride MW, Harris KJ, Sampson AK, McClure JD, Graham D, Young G, Holyoake TL, Girolami MA, Dominiczak AFet al., 2010, Predictive response-relevant clustering of expression data provides insights into disease processes, NUCLEIC ACIDS RESEARCH, Vol: 38, Pages: 6831-6840, ISSN: 0305-1048

JOURNAL ARTICLE

Psorakis I, Damoulas T, Girolami MA, 2010, Multiclass Relevance Vector Machines: Sparsity and Accuracy, IEEE TRANSACTIONS ON NEURAL NETWORKS, Vol: 21, Pages: 1588-1598, ISSN: 1045-9227

JOURNAL ARTICLE

Damoulas T, Girolami MA, 2008, Probabilistic multi-class multi-kernel learning: on protein fold recognition and remote homology detection, BIOINFORMATICS, Vol: 24, Pages: 1264-1270, ISSN: 1367-4803

JOURNAL ARTICLE

Overton IM, Padovani G, Girolami MA, Barton GJet al., 2008, ParCrys: a Parzen window density estimation approach to protein crystallization propensity prediction, BIOINFORMATICS, Vol: 24, Pages: 901-907, ISSN: 1367-4803

JOURNAL ARTICLE

Vyshemirsky V, Girolami MA, 2008, Bayesian ranking of biochemical system models, BIOINFORMATICS, Vol: 24, Pages: 833-839, ISSN: 1367-4803

JOURNAL ARTICLE

Fliser D, Novak J, Thongboonkerd V, Argiles A, Jankowski V, Girolami MA, Jankowski J, Mischak Het al., 2007, Advances in urinary proteome analysis and biomarker discovery, JOURNAL OF THE AMERICAN SOCIETY OF NEPHROLOGY, Vol: 18, Pages: 1057-1071, ISSN: 1046-6673

JOURNAL ARTICLE

Szymkowiak-Have A, Girolami MA, Larsen J, 2006, Clustering via kernel decomposition., IEEE Trans Neural Netw, Vol: 17, Pages: 256-264, ISSN: 1045-9227

Spectral clustering methods were proposed recently which rely on the eigenvalue decomposition of an affinity matrix. In this letter, the affinity matrix is created from the elements of a nonparametric density estimator and then decomposed to obtain posterior probabilities of class membership. Hyperparameters are selected using standard cross-validation methods.

JOURNAL ARTICLE

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