All Stories

  1. Markov Chain Monte Carlo from Lagrangian Dynamics

    Article • Journal of Computational and Graphical Statistics, April 2015, Taylor & Francis

    mark girolami

  2. Pseudo-Marginal Bayesian Inference for Gaussian Processes

    Article • IEEE Transactions on Pattern Analysis and Machine Intelligence, November 2014, Institute of Electrical & Electronics Engineers (IEEE)

    mark girolami

  3. A First Course in Machine Learning by Simon Rogers and Mark Girolami

    Article • International Statistical Review, April 2014, Wiley

    mark girolami

  4. Zero Variance Differential Geometric Markov Chain Monte Carlo Algorithms

    Article • Bayesian Analysis, March 2014, Institute of Mathematical Statistics

    mark girolami

  5. Discussion of the article ‘Geodesic Monte Carlo on Embedded Manifolds’ by Simon Byrne and Mark Girolami

    Article • Scandinavian Journal of Statistics, February 2014, Wiley

    mark girolami

  6. Rejoinder: Geodesic Monte Carlo on Embedded Manifolds

    Article • Scandinavian Journal of Statistics, January 2014, Wiley

    mark girolami

  7. The Silicon Trypanosome

    Article • January 2014, Elsevier

    mark girolami, Dr. Jurgen R Haanstra

  8. Geodesic Monte Carlo on Embedded Manifolds

    Article • Scandinavian Journal of Statistics, September 2013, Wiley

    mark girolami

  9. Handbook of Statistical Systems Biology by Michael P. H. Stumpf, David J. Balding, Mark Girolami

    Article • International Statistical Review, August 2013, Wiley

    mark girolami

  10. Automated, High Accuracy Classification of Parkinsonian Disorders: A Pattern Recognition Approach

    Article • PLOS One, July 2013, PLOS

    mark girolami

  11. Analysing user behaviour through dynamic population models

    Article • April 2013, ACM (Association for Computing Machinery)

    mark girolami

  12. Bayesian Approaches for Mechanistic Ion Channel Modeling

    Article • January 2013, Springer Science + Business Media

    mark girolami

  13. Markov chain Monte Carlo inference for Markov jump processes via the linear noise approximation

    Article • Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, December 2012, Royal Society Publishing

    mark girolami

  14. Unique Reporter-Based Sensor Platforms to Monitor Signalling in Cells

    Article • PLOS One, November 2012, PLOS

    mark girolami

  15. Markov Chain Monte Carlo Methods for State-Space Models with Point Process Observations

    Article • Neural Computation, June 2012, The MIT Press

    mark girolami

  16. Learning and Inference in Computational Systems Biology . Computational Molecular Biology. Edited by Neil D. Lawrence, Mark Girolami, Magnus Rattray, and Guido Sanguinetti. Cambridge (Massachusetts): MIT Press. $40.00. viii + 362 p.; ill.; index. ISBN...

    Article • The Quarterly Review of Biology, June 2012, University of Chicago Press

    mark girolami

  17. Handbook of Statistical Systems Biology

    Article • September 2011, Wiley

    mark girolami

  18. Population MCMC methods for history matching and uncertainty quantification

    Article • Computational Geosciences, May 2011, Springer Science + Business Media

    mark girolami

  19. Manifold MCMC for Mixtures

    Article • April 2011, Wiley

    mark girolami

  20. Riemann manifold Langevin and Hamiltonian Monte Carlo methods

    Article • Journal of the Royal Statistical Society Series B Statistical Methodology, March 2011, Oxford University Press (OUP)

    mark girolami

  21. Bayesian methods to detect dye-labelled DNA oligonucleotides in multiplexed Raman spectra

    Article • Journal of the Royal Statistical Society Series C (Applied Statistics), January 2011, Oxford University Press (OUP)

    mark girolami, Duncan Graham

  22. Addressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers

    Article • BMC Bioinformatics, December 2010, Springer Science + Business Media

    mark girolami

  23. Multiclass Relevance Vector Machines: Sparsity and Accuracy

    Article • IEEE Transactions on Neural Networks, October 2010, Institute of Electrical & Electronics Engineers (IEEE)

    mark girolami

  24. Combining Information with a Bayesian Multi-class Multi-kernel Pattern Recognition Machine

    Article • June 2010, World Scientific Pub Co Pte Lt

    mark girolami

  25. Infinite factorization of multiple non-parametric views

    Article • Machine Learning, November 2009, Springer Science + Business Media

    mark girolami

  26. Estimating Bayes factors via thermodynamic integration and population MCMC

    Article • Computational Statistics & Data Analysis, October 2009, Elsevier

    mark girolami

  27. Semi-parametric analysis of multi-rater data

    Article • Statistics and Computing, April 2009, Springer Science + Business Media

    mark girolami

  28. Pattern recognition with a Bayesian kernel combination machine

    Article • Pattern Recognition Letters, January 2009, Elsevier

    mark girolami

  29. Class Prediction from Disparate Biological Data Sources Using an Iterative Multi-Kernel Algorithm

    Article • January 2009, Springer Science + Business Media

    mark girolami

  30. Bayesian inference for differential equations

    Article • Theoretical Computer Science, November 2008, Elsevier

    mark girolami

  31. BRCA1 and BRCA2 Missense Variants of High and Low Clinical Significance Influence Lymphoblastoid Cell Line Post-Irradiation Gene Expression

    Article • PLoS Genetics, May 2008, PLOS

    mark girolami

  32. Inferring Sparse Kernel Combinations and Relevance Vectors: An Application to Subcellular Localization of Proteins

    Article • January 2008, Institute of Electrical & Electronics Engineers (IEEE)

    mark girolami

  33. Bayesian model-based inference of transcription factor activity

    Article • BMC Bioinformatics, May 2007, Springer Science + Business Media

    mark girolami

  34. Clinical proteomics: A need to define the field and to begin to set adequate standards

    Article • PROTEOMICS - CLINICAL APPLICATIONS, February 2007, Wiley

    mark girolami

  35. Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors

    Article • Neural Computation, August 2006, The MIT Press

    mark girolami

  36. Analysis of complex, multidimensional datasets

    Article • Drug Discovery Today Technologies, March 2006, Elsevier

    mark girolami

  37. Probabilistic hyperspace analogue to language

    Article • August 2005, ACM (Association for Computing Machinery)

    mark girolami

  38. Disease Classification from Capillary Electrophoresis: Mass Spectrometry

    Article • January 2005, Springer Science + Business Media

    mark girolami

  39. Hierarchic Bayesian models for kernel learning

    Article • January 2005, ACM (Association for Computing Machinery)

    mark girolami

  40. Novelty detection employing an L 2 optimal non-parametric density estimator

    Article • Pattern Recognition Letters, September 2004, Elsevier

    mark girolami

  41. On an equivalence between PLSI and LDA

    Article • July 2003, ACM (Association for Computing Machinery)

    mark girolami

  42. Orthogonal Series Density Estimation and the Kernel Eigenvalue Problem

    Article • Neural Computation, March 2002, The MIT Press

    mark girolami

  43. Kernel PCA for Feature Extraction and De-Noising in Nonlinear Regression

    Article • Neural Computing and Applications, December 2001, Springer Science + Business Media

    mark girolami

  44. A Variational Method for Learning Sparse and Overcomplete Representations

    Article • Neural Computation, November 2001, The MIT Press

    mark girolami

  45. An Expectation-Maximization Approach to Nonlinear Component Analysis

    Article • Neural Computation, March 2001, The MIT Press

    mark girolami

  46. Advances in Independent Component Analysis

    Article • January 2000

    mark girolami

  47. Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources

    Article • Neural Computation, February 1999, The MIT Press

    mark girolami

  48. Self-Organising Neural Networks

    Article • January 1999

    mark girolami

  49. An Alternative Perspective on Adaptive Independent Component Analysis Algorithms

    Article • Neural Computation, November 1998, The MIT Press

    mark girolami