15 results found
Chkrebtii OA, Campbell DA, Calderhead B, et al., 2016, Bayesian Solution Uncertainty Quantification for Differential Equations, BAYESIAN ANALYSIS, Vol: 11, Pages: 1239-1267, ISSN: 1931-6690
Epstein M, Calderhead B, Girolami MA, et al., 2016, Bayesian Statistical Inference in Ion-Channel Models with Exact Missed Event Correction, BIOPHYSICAL JOURNAL, Vol: 111, Pages: 333-348, ISSN: 0006-3495
Girolami MA, 2014, Big Bayes Stories: A Collection of Vignettes, STATISTICAL SCIENCE, Vol: 29, Pages: 97-97, ISSN: 0883-4237
Jiwaji M, Sandison ME, Reboud J, et al., 2014, Quantification of Functionalised Gold Nanoparticle-Targeted Knockdown of Gene Expression in HeLa Cells, PLOS ONE, Vol: 9, ISSN: 1932-6203
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
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.
Jiwaji M, Daly R, Gibriel A, et al., 2012, Unique Reporter-Based Sensor Platforms to Monitor Signalling in Cells, PLOS ONE, Vol: 7, ISSN: 1932-6203
Good DM, Zuerbig P, Argiles A, et 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
Hopcroft LEM, McBride MW, Harris KJ, et 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
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
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
Overton IM, Padovani G, Girolami MA, et al., 2008, ParCrys: a Parzen window density estimation approach to protein crystallization propensity prediction, BIOINFORMATICS, Vol: 24, Pages: 901-907, ISSN: 1367-4803
Vyshemirsky V, Girolami MA, 2008, Bayesian ranking of biochemical system models, BIOINFORMATICS, Vol: 24, Pages: 833-839, ISSN: 1367-4803
Fliser D, Novak J, Thongboonkerd V, et 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
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.
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