Riemann manifold Langevin and Hamiltonian Monte Carlo methods
- M. Girolami, B. Calderhead
- Computer ScienceJournal of the Royal Statistical Society: Series…
- 1 March 2011
The methodology proposed automatically adapts to the local structure when simulating paths across this manifold, providing highly efficient convergence and exploration of the target density, and substantial improvements in the time‐normalized effective sample size are reported when compared with alternative sampling approaches.
Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources
- Te-Won Lee, M. Girolami, T. Sejnowski
- Computer ScienceNeural Computation
- 1 February 1999
An extension of the infomax algorithm of Bell and Sejnowski (1995) is presented that is able blindly to separate mixed signals with sub- and supergaussian source distributions and is effective at separating artifacts such as eye blinks and line noise from weaker electrical signals that arise from sources in the brain.
Mercer kernel-based clustering in feature space
- M. Girolami
- Computer ScienceIEEE Trans. Neural Networks
- 1 May 2002
It is shown that the eigenvectors of a kernel matrix which defines the implicit mapping provides a means to estimate the number of clusters inherent within the data and a computationally simple iterative procedure is presented for the subsequent feature space partitioning of the data.
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes
- B. Calderhead, M. Girolami, Neil D. Lawrence
- Computer ScienceNIPS
- 8 December 2008
This work presents an accelerated sampling procedure which enables Bayesian inference of parameters in nonlinear ordinary and delay differential equations via the novel use of Gaussian processes (GP).
Geodesic Monte Carlo on Embedded Manifolds
- Simon Byrne, M. Girolami
- MathematicsScandinavian journal of statistics, theory and…
- 25 January 2013
Methods to simulate from probability distributions that themselves are defined on a manifold, with common examples being classes of distributions describing directional statistics, are considered.
Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Sub-Gaussian and Super-Gaussian Sources
- Te-Won Lee, M. Girolami, T. Sejnowski
- Computer ScienceNeural Computation
- 1999
Probability Density Estimation from Optimally Condensed Data Samples
- M. Girolami, Chao He
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine…
- 1 October 2003
The Reduced Set Density Estimator is presented, which provides a kernel-based density estimator which employs a small percentage of the available data sample and is optimal in the L/sub 2/ sense.
Blind source separation of more sources than mixtures using overcomplete representations
- Te-Won Lee, M. Lewicki, M. Girolami, T. Sejnowski
- EngineeringIEEE Signal Processing Letters
- 1 April 1999
It is demonstrated that three speech signals can be separated with good fidelity given only two mixtures of the three signals.
A Variational Method for Learning Sparse and Overcomplete Representations
- M. Girolami
- Computer ScienceNeural Computation
- 1 November 2001
An expectation-maximization algorithm for learning sparse and overcomplete data representations is presented. The proposed algorithm exploits a variational approximation to a range of heavy-tailed…
Naturally Occurring Human Urinary Peptides for Use in Diagnosis of Chronic Kidney Disease*
- D. Good, P. Zürbig, P. Schmitt‐Kopplin
- Biology, MedicineMolecular & Cellular Proteomics
- 8 July 2010
The establishment of a reproducible, high resolution method for peptidome analysis of naturally occurring human urinary peptides and proteins, ranging from 800 to 17,000 Da, using samples from 3,600 individuals analyzed by capillary electrophoresis coupled to MS is reported.
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