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  1. 1
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  2. 2
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  3. 3
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  5. 5
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  6. 6
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  7. 7
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  8. 8
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  9. 9
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  10. 10
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  11. 11
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  12. 12
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  14. 14
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  15. 15
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  16. 16
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  17. 17
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  18. 18
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  19. 19
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  23. 23
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  24. 24
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  25. 25
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  26. 26
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  27. 27
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  28. 28
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  29. 29
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  30. 30
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  31. 31
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  32. 32
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  33. 33
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  34. 34
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  35. 35
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  36. 36
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  37. 37
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  38. 38
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  39. 39
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  40. 40
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  41. 41
    P. Alquier, N. Friel, R. Everitt, A. Boland, Noisy Monte Carlo: convergence of Markov chains with approximate transition kernels, Statistics and Computing, 2014,

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  42. 42
    Joep Vanlier, Christian A Tiemann, Peter AJ Hilbers, Natal AW van Riel, Optimal experiment design for model selection in biochemical networks, BMC Systems Biology, 2014, 8, 1, 20

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  43. 43
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  44. 44
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  45. 45
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  46. 46
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  47. 47
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  48. 48
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  49. 49
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  50. 50
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  51. 51
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    CrossRef

  52. 52
    R. Talmon, R. R. Coifman, Empirical intrinsic geometry for nonlinear modeling and time series filtering, Proceedings of the National Academy of Sciences, 2013, 110, 31, 12535

    CrossRef

  53. 53
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    CrossRef

  54. 54
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  55. 55
    S. Hug, A. Raue, J. Hasenauer, J. Bachmann, U. Klingmüller, J. Timmer, F.J. Theis, High-dimensional Bayesian parameter estimation: Case study for a model of JAK2/STAT5 signaling, Mathematical Biosciences, 2013, 246, 2, 293

    CrossRef

  56. 56
    Andrea Arnold, Daniela Calvetti, Erkki Somersalo, Linear multistep methods, particle filtering and sequential Monte Carlo, Inverse Problems, 2013, 29, 8, 085007

    CrossRef

  57. 57
    GIORGOS SERMAIDIS, OMIROS PAPASPILIOPOULOS, GARETH O. ROBERTS, ALEXANDROS BESKOS, PAUL FEARNHEAD, Markov Chain Monte Carlo for Exact Inference for Diffusions, Scandinavian Journal of Statistics, 2013, 40, 2
  58. 58
    J. Vanlier, C.A. Tiemann, P.A.J. Hilbers, N.A.W. van Riel, Parameter uncertainty in biochemical models described by ordinary differential equations, Mathematical Biosciences, 2013, 246, 2, 305

    CrossRef

  59. 59
    A. F. Villaverde, J. R. Banga, Reverse engineering and identification in systems biology: strategies, perspectives and challenges, Journal of The Royal Society Interface, 2013, 11, 91, 20130505

    CrossRef

  60. 60
    Marta Blangiardo, Michela Cameletti, Gianluca Baio, Håvard Rue, Spatial and spatio-temporal models with R-INLA, Spatial and Spatio-temporal Epidemiology, 2013, 4, 33

    CrossRef

  61. 61
    Marta Blangiardo, Michela Cameletti, Gianluca Baio, Håvard Rue, Spatial and spatio-temporal models with R-INLA, Spatial and Spatio-temporal Epidemiology, 2013, 7, 39

    CrossRef

  62. 62
    Antonietta Mira, Reza Solgi, Daniele Imparato, Zero variance Markov chain Monte Carlo for Bayesian estimators, Statistics and Computing, 2013, 23, 5, 653

    CrossRef

  63. 63
    J. Vanlier, C. A. Tiemann, P. A. J. Hilbers, N. A. W. van Riel, A Bayesian approach to targeted experiment design, Bioinformatics, 2012, 28, 8, 1136

    CrossRef

  64. 64
    James Martin, Lucas C. Wilcox, Carsten Burstedde, Omar Ghattas, A Stochastic Newton MCMC Method for Large-Scale Statistical Inverse Problems with Application to Seismic Inversion, SIAM Journal on Scientific Computing, 2012, 34, 3, A1460

    CrossRef

  65. 65
    Virginia Recta, Murali Haran, James L. Rosenberger, A two-stage model for incidence and prevalence in point-level spatial count data, Environmetrics, 2012, 23, 2
  66. 66
    J. Vanlier, C. A. Tiemann, P. A. J. Hilbers, N. A. W. van Riel, An integrated strategy for prediction uncertainty analysis, Bioinformatics, 2012, 28, 8, 1130

    CrossRef

  67. 67
    Tarek A. El Moselhy, Youssef M. Marzouk, Bayesian inference with optimal maps, Journal of Computational Physics, 2012, 231, 23, 7815

    CrossRef

  68. 68
    Michalis K Titsias, Antti Honkela, Neil D Lawrence, Magnus Rattray, Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model comparison, BMC Systems Biology, 2012, 6, 1, 53

    CrossRef

  69. 69
    A. Raue, C. Kreutz, F. J. Theis, J. Timmer, Joining forces of Bayesian and frequentist methodology: a study for inference in the presence of non-identifiability, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2012, 371, 1984, 20110544

    CrossRef

  70. 70
    V. Stathopoulos, M. A. Girolami, 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, 2012, 371, 1984, 20110541

    CrossRef

  71. 71
    Ke Yuan, Mark Girolami, Mahesan Niranjan, Markov Chain Monte Carlo Methods for State-Space Models with Point Process Observations, Neural Computation, 2012, 24, 6, 1462

    CrossRef

  72. 72
    F. Rigat, A. Mira, Parallel hierarchical sampling: A general-purpose interacting Markov chains Monte Carlo algorithm, Computational Statistics & Data Analysis, 2012, 56, 6, 1450

    CrossRef

  73. 73
    Linah Mohamed, Ben Calderhead, Maurizio Filippone, Mike Christie, Mark Girolami, Population MCMC methods for history matching and uncertainty quantification, Computational Geosciences, 2012, 16, 2, 423

    CrossRef

  74. 74
    Jarno Vanhatalo, Lari Veneranta, Richard Hudd, Species distribution modeling with Gaussian processes: A case study with the youngest stages of sea spawning whitefish (Coregonus lavaretus L. s.l.) larvae, Ecological Modelling, 2012, 228, 49

    CrossRef

  75. 75
    Bayesian Methods in Health Economics, 2012,

    CrossRef

  76. 76
    M. Filippone, A. Mira, M. Girolami, Discussion of the paper: “Sampling schemes for generalized linear Dirichlet process random effects models” by M. Kyung, J. Gill, and G. Casella, Statistical Methods & Applications, 2011, 20, 3, 295

    CrossRef

  77. 77
    Ender Konukoglu, Jatin Relan, Ulas Cilingir, Bjoern H. Menze, Phani Chinchapatnam, Amir Jadidi, Hubert Cochet, Mélèze Hocini, Hervé Delingette, Pierre Jaïs, Michel Haïssaguerre, Nicholas Ayache, Maxime Sermesant, Efficient probabilistic model personalization integrating uncertainty on data and parameters: Application to Eikonal-Diffusion models in cardiac electrophysiology, Progress in Biophysics and Molecular Biology, 2011, 107, 1, 134

    CrossRef

  78. 78
    A. Beskos, F.J. Pinski, J.M. Sanz-Serna, A.M. Stuart, Hybrid Monte Carlo on Hilbert spaces, Stochastic Processes and their Applications, 2011, 121, 10, 2201

    CrossRef

  79. 79
    B. Calderhead, M. Girolami, Statistical analysis of nonlinear dynamical systems using differential geometric sampling methods, Interface Focus, 2011, 1, 6, 821

    CrossRef

  80. 80
    Bibliography,
  81. 81
    Mike Christie, Uncertainty Quantification and Oil Reservoir Modelling,