mark girolami
University of Warwick
Professor, Statistics
United Kingdom
My co-authors include
Duncan Graham
Dr. Jurgen R Haanstra
My Publications
Markov Chain Monte Carlo from Lagrangian Dynamics
Journal of Computational and Graphical Statistics
April 2015
Pseudo-Marginal Bayesian Inference for Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
November 2014
A First Course in Machine Learning by Simon Rogers and Mark Girolami
International Statistical Review
April 2014
Zero Variance Differential Geometric Markov Chain Monte Carlo Algorithms
Bayesian Analysis
March 2014
Discussion of the article ‘Geodesic Monte Carlo on Embedded Manifolds’ by Simon Byrne a...
Scandinavian Journal of Statistics
February 2014
Rejoinder: Geodesic Monte Carlo on Embedded Manifolds
Scandinavian Journal of Statistics
January 2014
The Silicon Trypanosome
January 2014
Geodesic Monte Carlo on Embedded Manifolds
Scandinavian Journal of Statistics
September 2013
Handbook of Statistical Systems Biology by Michael P. H. Stumpf, David J. Balding, Mark...
International Statistical Review
August 2013
Automated, High Accuracy Classification of Parkinsonian Disorders: A Pattern Recognitio...
PLoS ONE
July 2013
Bayesian Approaches for Mechanistic Ion Channel Modeling
January 2013
Analysing user behaviour through dynamic population models
January 2013
Markov chain Monte Carlo inference for Markov jump processes via the linear noise appro...
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering...
December 2012
Unique Reporter-Based Sensor Platforms to Monitor Signalling in Cells
PLoS ONE
November 2012
Markov Chain Monte Carlo Methods for State-Space Models with Point Process Observations
Neural Computation
June 2012
Learning and Inference in Computational Systems Biology . Computational Molecular Biol...
The Quarterly Review of Biology
June 2012
Handbook of Statistical Systems Biology
September 2011
Population MCMC methods for history matching and uncertainty quantification
Computational Geosciences
May 2011
Manifold MCMC for Mixtures
April 2011
Riemann manifold Langevin and Hamiltonian Monte Carlo methods
Journal of the Royal Statistical Society Series B (Statistical Methodology)
March 2011
Bayesian methods to detect dye-labelled DNA oligonucleotides in multiplexed Raman spectra
Journal of the Royal Statistical Society Series C (Applied Statistics)
January 2011
Multiclass Relevance Vector Machines: Sparsity and Accuracy
IEEE Transactions on Neural Networks
October 2010
Combining Information with a Bayesian Multi-class Multi-kernel Pattern Recognition Machine
June 2010
Addressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers
BMC Bioinformatics
January 2010
Infinite factorization of multiple non-parametric views
Machine Learning
November 2009
Estimating Bayes factors via thermodynamic integration and population MCMC
Computational Statistics & Data Analysis
October 2009
Semi-parametric analysis of multi-rater data
Statistics and Computing
April 2009
Pattern recognition with a Bayesian kernel combination machine
Pattern Recognition Letters
January 2009
Class Prediction from Disparate Biological Data Sources Using an Iterative Multi-Kernel...
January 2009
Bayesian inference for differential equations
Theoretical Computer Science
November 2008
BRCA1 and BRCA2 Missense Variants of High and Low Clinical Significance Influence Lymph...
PLoS Genetics
May 2008
Inferring Sparse Kernel Combinations and Relevance Vectors: An Application to Subcellul...
January 2008
Bayesian model-based inference of transcription factor activity
BMC Bioinformatics
May 2007
Clinical proteomics: A need to define the field and to begin to set adequate standards
PROTEOMICS - CLINICAL APPLICATIONS
February 2007
Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors
Neural Computation
August 2006
Analysis of complex, multidimensional datasets
Drug Discovery Today Technologies
March 2006
Disease Classification from Capillary Electrophoresis: Mass Spectrometry
January 2005
Hierarchic Bayesian models for kernel learning
January 2005
Probabilistic hyperspace analogue to language
January 2005
Novelty detection employing an L 2 optimal non-parametric density estimator
Pattern Recognition Letters
September 2004
On an equivalence between PLSI and LDA
January 2003
Orthogonal Series Density Estimation and the Kernel Eigenvalue Problem
Neural Computation
March 2002
Kernel PCA for Feature Extraction and De-Noising in Nonlinear Regression
Neural Computing and Applications
December 2001
A Variational Method for Learning Sparse and Overcomplete Representations
Neural Computation
November 2001
An Expectation-Maximization Approach to Nonlinear Component Analysis
Neural Computation
March 2001
Advances in Independent Component Analysis
January 2000
Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussia...
Neural Computation
February 1999
Self-Organising Neural Networks
January 1999
An Alternative Perspective on Adaptive Independent Component Analysis Algorithms
Neural Computation
November 1998
