All Stories

  1. Markov Chain Monte Carlo from Lagrangian Dynamics
  2. Pseudo-Marginal Bayesian Inference for Gaussian Processes
  3. A First Course in Machine Learning by Simon Rogers and Mark Girolami
  4. Zero Variance Differential Geometric Markov Chain Monte Carlo Algorithms
  5. Discussion of the article ‘Geodesic Monte Carlo on Embedded Manifolds’ by Simon Byrne and Mark Girolami
  6. Rejoinder: Geodesic Monte Carlo on Embedded Manifolds
  7. The Silicon Trypanosome
  8. Geodesic Monte Carlo on Embedded Manifolds
  9. Handbook of Statistical Systems Biology by Michael P. H. Stumpf, David J. Balding, Mark Girolami
  10. Automated, High Accuracy Classification of Parkinsonian Disorders: A Pattern Recognition Approach
  11. Analysing user behaviour through dynamic population models
  12. Bayesian Approaches for Mechanistic Ion Channel Modeling
  13. Markov chain Monte Carlo inference for Markov jump processes via the linear noise approximation
  14. Unique Reporter-Based Sensor Platforms to Monitor Signalling in Cells
  15. Markov Chain Monte Carlo Methods for State-Space Models with Point Process Observations
  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...
  17. Handbook of Statistical Systems Biology
  18. Population MCMC methods for history matching and uncertainty quantification
  19. Manifold MCMC for Mixtures
  20. Riemann manifold Langevin and Hamiltonian Monte Carlo methods
  21. Bayesian methods to detect dye-labelled DNA oligonucleotides in multiplexed Raman spectra
  22. Addressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers
  23. Multiclass Relevance Vector Machines: Sparsity and Accuracy
  24. Combining Information with a Bayesian Multi-class Multi-kernel Pattern Recognition Machine
  25. Infinite factorization of multiple non-parametric views
  26. Estimating Bayes factors via thermodynamic integration and population MCMC
  27. Semi-parametric analysis of multi-rater data
  28. Pattern recognition with a Bayesian kernel combination machine
  29. Class Prediction from Disparate Biological Data Sources Using an Iterative Multi-Kernel Algorithm
  30. Bayesian inference for differential equations
  31. BRCA1 and BRCA2 Missense Variants of High and Low Clinical Significance Influence Lymphoblastoid Cell Line Post-Irradiation Gene Expression
  32. Inferring Sparse Kernel Combinations and Relevance Vectors: An Application to Subcellular Localization of Proteins
  33. Bayesian model-based inference of transcription factor activity
  34. Clinical proteomics: A need to define the field and to begin to set adequate standards
  35. Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors
  36. Analysis of complex, multidimensional datasets
  37. Probabilistic hyperspace analogue to language
  38. Disease Classification from Capillary Electrophoresis: Mass Spectrometry
  39. Hierarchic Bayesian models for kernel learning
  40. Novelty detection employing an L 2 optimal non-parametric density estimator
  41. On an equivalence between PLSI and LDA
  42. Orthogonal Series Density Estimation and the Kernel Eigenvalue Problem
  43. Kernel PCA for Feature Extraction and De-Noising in Nonlinear Regression
  44. A Variational Method for Learning Sparse and Overcomplete Representations
  45. An Expectation-Maximization Approach to Nonlinear Component Analysis
  46. Advances in Independent Component Analysis
  47. Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources
  48. Self-Organising Neural Networks
  49. An Alternative Perspective on Adaptive Independent Component Analysis Algorithms