SEARCH

SEARCH BY CITATION

Cited in:

CrossRef

This article has been cited by:

  1. 1
    Libo Sun, Chihoon Lee, Jennifer A. Hoeting, A penalized simulated maximum likelihood approach in parameter estimation for stochastic differential equations, Computational Statistics & Data Analysis, 2015, 84, 54

    CrossRef

  2. 2
    Eugenia Koblents, Joaquín Míguez, A population Monte Carlo scheme with transformed weights and its application to stochastic kinetic models, Statistics and Computing, 2015, 25, 2, 407

    CrossRef

  3. 3
    Adam Persing, Ajay Jasra, Alexandros Beskos, David Balding, Maria De Iorio, A Simulation Approach for Change-Points on Phylogenetic Trees, Journal of Computational Biology, 2015, 22, 1, 10

    CrossRef

  4. 4
    Umberto Picchini, Julie Lyng Forman, Accelerating inference for diffusions observed with measurement error and large sample sizes using approximate Bayesian computation, Journal of Statistical Computation and Simulation, 2015, 1

    CrossRef

  5. 5
    Gianluca Mastrantonio, Antonello Maruotti, Giovanna Jona-Lasinio, Bayesian hidden Markov modelling using circular-linear general projected normal distribution, Environmetrics, 2015, 26, 1
  6. 6
    Edward L. Ionides, Dao Nguyen, Yves Atchadé, Stilian Stoev, Aaron A. King, Inference for dynamic and latent variable models via iterated, perturbed Bayes maps, Proceedings of the National Academy of Sciences, 2015, 112, 3, 719

    CrossRef

  7. 7
    Randal Douc, Florian Maire, Jimmy Olsson, On the use of Markov chain Monte Carlo methods for the sampling of mixture models: a statistical perspective, Statistics and Computing, 2015, 25, 1, 95

    CrossRef

  8. 8
    Johan Dahlin, Fredrik Lindsten, Thomas B. Schön, Particle Metropolis–Hastings using gradient and Hessian information, Statistics and Computing, 2015, 25, 1, 81

    CrossRef

  9. 9
    S. R. White, T. Kypraios, S. P. Preston, Piecewise Approximate Bayesian Computation: fast inference for discretely observed Markov models using a factorised posterior distribution, Statistics and Computing, 2015, 25, 2, 289

    CrossRef

  10. 10
    Simo Särkkä, Jouni Hartikainen, Isambi Sailon Mbalawata, Heikki Haario, Posterior inference on parameters of stochastic differential equations via non-linear Gaussian filtering and adaptive MCMC, Statistics and Computing, 2015, 25, 2, 427

    CrossRef

  11. 11
    Jamie Owen, Darren J. Wilkinson, Colin S. Gillespie, Scalable inference for Markov processes with intractable likelihoods, Statistics and Computing, 2015, 25, 1, 145

    CrossRef

  12. 12
    A. Fulop, J. Li, J. Yu, Self-Exciting Jumps, Learning, and Asset Pricing Implications, Review of Financial Studies, 2015, 28, 3, 876

    CrossRef

  13. 13
    Mohammad Khalil, Abhijit Sarkar, Sondipon Adhikari, Dominique Poirel, The estimation of time-invariant parameters of noisy nonlinear oscillatory systems, Journal of Sound and Vibration, 2015, 344, 81

    CrossRef

  14. 14
    Fredrik Lindsten, Randal Douc, Eric Moulines, Uniform Ergodicity of the Particle Gibbs Sampler, Scandinavian Journal of Statistics, 2015, 42, 1
  15. 15
    S. Peluso, F. Corsi, A. Mira, A Bayesian High-Frequency Estimator of the Multivariate Covariance of Noisy and Asynchronous Returns, Journal of Financial Econometrics, 2014,

    CrossRef

  16. 16
    Alberto Cozzini, Ajay Jasra, Giovanni Montana, Adam Persing, A Bayesian mixture of lasso regressions with -errors, Computational Statistics & Data Analysis, 2014, 77, 84

    CrossRef

  17. 17
    Diego P. Ruiz, Fabrizio Ruggeri, Sara Pasquali, Joaquín Míguez, Ettore Lanzarone, Gianni Gilioli, Laura Martín-Fernández, A Rao-Blackwellized particle filter for joint parameter estimation and biomass tracking in a stochastic predator-prey system, Mathematical Biosciences and Engineering, 2014, 11, 3, 573

    CrossRef

  18. 18
    Michael Dowd, Emlyn Jones, John Parslow, A statistical overview and perspectives on data assimilation for marine biogeochemical models, Environmetrics, 2014, 25, 4
  19. 19
    Anindya Bhadra, Edward L. Ionides, Adaptive particle allocation in iterated sequential Monte Carlo via approximating meta-models, Statistics and Computing, 2014,

    CrossRef

  20. 20
    Julien Cornebise, Eric Moulines, Jimmy Olsson, Adaptive sequential Monte Carlo by means of mixture of experts, Statistics and Computing, 2014, 24, 3, 317

    CrossRef

  21. 21
    Yuan Li, Dongryeol Ryu, Andrew W. Western, Q.J. Wang, David E. Robertson, Wade T. Crow, An integrated error parameter estimation and lag-aware data assimilation scheme for real-time flood forecasting, Journal of Hydrology, 2014, 519, 2722

    CrossRef

  22. 22
    Ajay Jasra, Approximate Bayesian Computation for a Class of Time Series Models, International Statistical Review, 2014, 82, 3
  23. 23
    James S. Martin, Ajay Jasra, Sumeetpal S. Singh, Nick Whiteley, Pierre Del Moral, Emma McCoy, Approximate Bayesian Computation for Smoothing, Stochastic Analysis and Applications, 2014, 32, 3, 397

    CrossRef

  24. 24
    Ajay Jasra, Nikolas Kantas, Elena Ehrlich, Approximate Inference for Observation-Driven Time Series Models with Intractable Likelihoods, ACM Transactions on Modeling and Computer Simulation, 2014, 24, 3, 1

    CrossRef

  25. 25
    Ullrika Sahlin, Assessment of uncertainty in chemical models by Bayesian probabilities: Why, when, how?, Journal of Computer-Aided Molecular Design, 2014,

    CrossRef

  26. 26
    Jamie Hall, Michael K. Pitt, Robert Kohn, Bayesian inference for nonlinear structural time series models, Journal of Econometrics, 2014, 179, 2, 99

    CrossRef

  27. 27
    Ajay Jasra, Nikolas Kantas, Adam Persing, Bayesian parameter inference for partially observed stopped processes, Statistics and Computing, 2014, 24, 1, 1

    CrossRef

  28. 28
    Sinan Yıldırım, Lan Jiang, Sumeetpal S. Singh, Thomas A. Dean, Calibrating the Gaussian multi-target tracking model, Statistics and Computing, 2014,

    CrossRef

  29. 29
    Daniel M. Sheinson, Jarad Niemi, Wendy Meiring, Comparison of the performance of particle filter algorithms applied to tracking of a disease epidemic, Mathematical Biosciences, 2014, 255, 21

    CrossRef

  30. 30
    Junshan Wang, Ajay Jasra, Maria De Iorio, Computational Methods for a Class of Network Models, Journal of Computational Biology, 2014, 21, 2, 141

    CrossRef

  31. 31
    Andrew Golightly, Daniel A. Henderson, Chris Sherlock, Delayed acceptance particle MCMC for exact inference in stochastic kinetic models, Statistics and Computing, 2014,

    CrossRef

  32. 32
    Radu Herbei, L. Mark Berliner, Estimating Ocean Circulation: An MCMC Approach With Approximated Likelihoods via the Bernoulli Factory, Journal of the American Statistical Association, 2014, 109, 507, 944

    CrossRef

  33. 33
    Simon Barthelmé, Nicolas Chopin, Expectation Propagation for Likelihood-Free Inference, Journal of the American Statistical Association, 2014, 109, 505, 315

    CrossRef

  34. 34
    Tiancheng Li, Shudong Sun, Tariq Pervez Sattar, Juan Manuel Corchado, Fight sample degeneracy and impoverishment in particle filters: A review of intelligent approaches, Expert Systems with Applications, 2014, 41, 8, 3944

    CrossRef

  35. 35
    Bo Wang, Jian Qing Shi, Generalized Gaussian Process Regression Model for Non-Gaussian Functional Data, Journal of the American Statistical Association, 2014, 109, 507, 1123

    CrossRef

  36. 36
    Fabiana C. Hamilton, Marcelo J. Colaço, Rogério N. Carvalho, Albino J.K. Leiroz, Heat transfer coefficient estimation of an internal combustion engine using particle filters, Inverse Problems in Science and Engineering, 2014, 22, 3, 483

    CrossRef

  37. 37
    Francisco Madrigal, Jean-Bernard Hayet, Frédéric Lerasle, Improving multiple pedestrians tracking with semantic information, Signal, Image and Video Processing, 2014, 8, S1, 113

    CrossRef

  38. 38
    Umberto Picchini, Inference for SDE Models via Approximate Bayesian Computation, Journal of Computational and Graphical Statistics, 2014, 23, 4, 1080

    CrossRef

  39. 39
    Geoffrey R. Hosack, Gareth W. Peters, Stuart A. Ludsin, Kenneth Rose, Interspecific relationships and environmentally driven catchabilities estimated from fisheries data, Canadian Journal of Fisheries and Aquatic Sciences, 2014, 71, 3, 447

    CrossRef

  40. 40
    Andreas S. Stordal, Iterative Bayesian inversion with Gaussian mixtures: finite sample implementation and large sample asymptotics, Computational Geosciences, 2014,

    CrossRef

  41. 41
    Gianluigi Pillonetto, Francesco Dinuzzo, Tianshi Chen, Giuseppe De Nicolao, Lennart Ljung, Kernel methods in system identification, machine learning and function estimation: A survey, Automatica, 2014, 50, 3, 657

    CrossRef

  42. 42
    Luc Bauwens, Arnaud Dufays, Jeroen V.K. Rombouts, Marginal likelihood for Markov-switching and change-point GARCH models, Journal of Econometrics, 2014, 178, 508

    CrossRef

  43. 43
    Carles Bretó, On idiosyncratic stochasticity of financial leverage effects, Statistics & Probability Letters, 2014, 91, 20

    CrossRef

  44. 44
    Markéta Zikmundová, Kateřina Staňková Helisová, Viktor Beneš, On the Use of Particle Markov Chain Monte Carlo in Parameter Estimation of Space-Time Interacting Discs, Methodology and Computing in Applied Probability, 2014, 16, 2, 451

    CrossRef

  45. 45
    Lyudmila Mihaylova, Avishy Y. Carmi, François Septier, Amadou Gning, Sze Kim Pang, Simon Godsill, Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking, Digital Signal Processing, 2014, 25, 1

    CrossRef

  46. 46
    B. Mirauta, P. Nicolas, H. Richard, Parseq: reconstruction of microbial transcription landscape from RNA-Seq read counts using state-space models, Bioinformatics, 2014, 30, 10, 1409

    CrossRef

  47. 47
    P Minvielle, A Todeschini, F Caron, P Del Moral, Particle MCMC for Bayesian Microwave Control, Journal of Physics: Conference Series, 2014, 542, 012007

    CrossRef

  48. 48
    Simon van Mourik, Cajo ter Braak, Hans Stigter, Jaap Molenaar, Prediction uncertainty assessment of a systems biology model requires a sample of the full probability distribution of its parameters, PeerJ, 2014, 2, e433

    CrossRef

  49. 49
    Jianzhong Sun, Hongfu Zuo, Wenbin Wang, Michael G. Pecht, Prognostics uncertainty reduction by fusing on-line monitoring data based on a state-space-based degradation model, Mechanical Systems and Signal Processing, 2014, 45, 2, 396

    CrossRef

  50. 50
    Hulin Wu, Hongyu Miao, Hongqi Xue, David J. Topham, Martin Zand, Quantifying Immune Response to Influenza Virus Infection via Multivariate Nonlinear ODE Models with Partially Observed State Variables and Time-Varying Parameters, Statistics in Biosciences, 2014,

    CrossRef

  51. 51
    D. Clifford, D. Pagendam, J. Baldock, N. Cressie, R. Farquharson, M. Farrell, L. Macdonald, L. Murray, Rethinking soil carbon modelling: a stochastic approach to quantify uncertainties, Environmetrics, 2014, 25, 4
  52. 52
    F. Din-Houn Lau, Axel Gandy, RMCMC: A system for updating Bayesian models, Computational Statistics & Data Analysis, 2014, 80, 99

    CrossRef

  53. 53
    Junjing Lin, Michael Ludkovski, Sequential Bayesian inference in hidden Markov stochastic kinetic models with application to detection and response to seasonal epidemics, Statistics and Computing, 2014, 24, 6, 1047

    CrossRef

  54. 54
    T. Déirdre Hollingsworth, Juliet R.C. Pulliam, Sebastian Funk, James E. Truscott, Valerie Isham, Alun L. Lloyd, Seven challenges for modelling indirect transmission: Vector-borne diseases, macroparasites and neglected tropical diseases, Epidemics, 2014,

    CrossRef

  55. 55
    Trevelyan J. McKinley, Joshua V. Ross, Rob Deardon, Alex R. Cook, Simulation-based Bayesian inference for epidemic models, Computational Statistics & Data Analysis, 2014, 71, 434

    CrossRef

  56. 56
    Patrick E. Brown, Florencia Chimard, Alexander Remorov, Jeffrey S. Rosenthal, Xin Wang, Statistical inference and computational efficiency for spatial infectious disease models with plantation data, Journal of the Royal Statistical Society: Series C (Applied Statistics), 2014, 63, 3
  57. 57
    D. J. Spiegelhalter, The future lies in uncertainty, Science, 2014, 345, 6194, 264

    CrossRef

  58. 58
    Adam Persin, Ajay Jasr, Twisting the Alive Particle Filter, Methodology and Computing in Applied Probability, 2014,

    CrossRef

  59. 59
    Garland Durham, John Geweke, Bayesian Model Comparison, 2014,

    CrossRef

  60. 60
    Gianluigi Pillonetto, Giuseppe De Nicolao, Modelling Methodology for Physiology and Medicine, 2014,

    CrossRef

  61. 61
    Simon Godsill, Academic Press Library in Signal Processing: Volume 3 - Array and Statistical Signal Processing, 2014,

    CrossRef

  62. 62
    J. Rougier, 'Intractable and unsolved': some thoughts on statistical data assimilation with uncertain static parameters, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2013, 371, 1991, 20120297

    CrossRef

  63. 63
    Sophie Donnet, Adeline Samson, A review on estimation of stochastic differential equations for pharmacokinetic/pharmacodynamic models, Advanced Drug Delivery Reviews, 2013, 65, 7, 929

    CrossRef

  64. 64
    Alexandros Beskos, Konstantinos Kalogeropoulos, Erik Pazos, Advanced MCMC methods for sampling on diffusion pathspace, Stochastic Processes and their Applications, 2013, 123, 4, 1415

    CrossRef

  65. 65
    Antti Solonen, Heikki Järvinen, An approach for tuning ensemble prediction systems, Tellus A, 2013, 65, 0

    CrossRef

  66. 66
    Georgios Karagiannis, Christophe Andrieu, Annealed Importance Sampling Reversible Jump MCMC Algorithms, Journal of Computational and Graphical Statistics, 2013, 22, 3, 623

    CrossRef

  67. 67
    Yuan Li, Dongryeol Ryu, Andrew W. Western, Q. J. Wang, Assimilation of stream discharge for flood forecasting: The benefits of accounting for routing time lags, Water Resources Research, 2013, 49, 4
  68. 68
    John Parslow, Noel Cressie, Edward P. Campbell, Emlyn Jones, Lawrence Murray, Bayesian learning and predictability in a stochastic nonlinear dynamical model, Ecological Applications, 2013, 23, 4, 679

    CrossRef

  69. 69
    Fredrik Lindsten, Thomas B. Schön, Michael I. Jordan, Bayesian semiparametric Wiener system identification, Automatica, 2013, 49, 7, 2053

    CrossRef

  70. 70
    Gareth William Peters, Mark Briers, Pavel Shevchenko, Arnaud Doucet, Calibration and Filtering for Multi Factor Commodity Models with Seasonality: Incorporating Panel Data from Futures Contracts, Methodology and Computing in Applied Probability, 2013, 15, 4, 841

    CrossRef

  71. 71
    J. Dureau, K. Kalogeropoulos, M. Baguelin, Capturing the time-varying drivers of an epidemic using stochastic dynamical systems, Biostatistics, 2013, 14, 3, 541

    CrossRef

  72. 72
    Jonathan Briggs, Michael Dowd, Renate Meyer, Data assimilation for large-scale spatio-temporal systems using a location particle smoother, Environmetrics, 2013, 24, 2
  73. 73
    Andras Fulop, Junye Li, Efficient learning via simulation: A marginalized resample-move approach, Journal of Econometrics, 2013, 176, 2, 146

    CrossRef

  74. 74
    Rahman Farnoosh, Arezoo Hajrajabi, Estimation of parameters in the state space model of stochastic RL electrical circuit, COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, 2013, 32, 3, 1082

    CrossRef

  75. 75
    David Lunn, Jessica Barrett, Michael Sweeting, Simon Thompson, Fully Bayesian hierarchical modelling in two stages, with application to meta-analysis, Journal of the Royal Statistical Society: Series C (Applied Statistics), 2013, 62, 4
  76. 76
    Jasper A. Vrugt, Cajo J.F. ter Braak, Cees G.H. Diks, Gerrit Schoups, Hydrologic data assimilation using particle Markov chain Monte Carlo simulation: Theory, concepts and applications, Advances in Water Resources, 2013, 51, 457

    CrossRef

  77. 77
    Brad Weir, Robert N. Miller, Yvette H. Spitz, Implicit Estimation of Ecological Model Parameters, Bulletin of Mathematical Biology, 2013, 75, 2, 223

    CrossRef

  78. 78
    Douglas A. Plaza Guingla, Robin Keyser, Gabriëlle J. M. Lannoy, Laura Giustarini, Patrick Matgen, Valentijn R. N. Pauwels, Improving particle filters in rainfall-runoff models: Application of the resample-move step and the ensemble Gaussian particle filter, Water Resources Research, 2013, 49, 7
  79. 79
    Márcio Poletti Laurini, Luiz Koodi Hotta, Indirect Inference in fractional short-term interest rate diffusions, Mathematics and Computers in Simulation, 2013, 94, 109

    CrossRef

  80. 80
    Adam Persing, Ajay Jasra, Likelihood computation for hidden Markov models via generalized two-filter smoothing, Statistics & Probability Letters, 2013, 83, 5, 1433

    CrossRef

  81. 81
    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
  82. 82
    Lawrence M. Murray, Emlyn M. Jones, John Parslow, On Disturbance State-Space Models and the Particle Marginal Metropolis-Hastings Sampler, SIAM/ASA Journal on Uncertainty Quantification, 2013, 1, 1, 494

    CrossRef

  83. 83
    Isambi S. Mbalawata, Simo Särkkä, Heikki Haario, Parameter estimation in stochastic differential equations with Markov chain Monte Carlo and non-linear Kalman filtering, Computational Statistics, 2013, 28, 3, 1195

    CrossRef

  84. 84
    Salima El Kolei, Parametric estimation of hidden stochastic model by contrast minimization and deconvolution, Metrika, 2013, 76, 8, 1031

    CrossRef

  85. 85
    Raquel Prado, Sequential estimation of mixtures of structured autoregressive models, Computational Statistics & Data Analysis, 2013, 58, 58

    CrossRef

  86. 86
    N. Chopin, P. E. Jacob, O. Papaspiliopoulos, SMC2: an efficient algorithm for sequential analysis of state space models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2013, 75, 3
  87. 87
    Geoffrey R. Hosack, Verena M. Trenkel, Jeffrey M. Dambacher, The relative importance of environmental stochasticity, interspecific interactions, and observation error: Insights from sardine and anchovy landings, Journal of Marine Systems, 2013, 125, 77

    CrossRef

  88. 88
    Markus Krauss, Rolf Burghaus, Jörg Lippert, Mikko Niemi, Pertti Neuvonen, Andreas Schuppert, Stefan Willmann, Lars Kuepfer, Linus Görlitz, Using Bayesian-PBPK modeling for assessment of inter-individual variability and subgroup stratification, In Silico Pharmacology, 2013, 1, 1, 6

    CrossRef

  89. 89
    Sydney C. Ludvigson, 2013,

    CrossRef

  90. 90
    A.J. Haug, Bayesian estimation for target tracking, Part III: Monte Carlo filters, Wiley Interdisciplinary Reviews: Computational Statistics, 2012, 4, 5
  91. 91
    Joerg Rings, Jasper A. Vrugt, Gerrit Schoups, Johan A. Huisman, Harry Vereecken, Bayesian model averaging using particle filtering and Gaussian mixture modeling: Theory, concepts, and simulation experiments, Water Resources Research, 2012, 48, 5
  92. 92
    Richard G. Everitt, Bayesian Parameter Estimation for Latent Markov Random Fields and Social Networks, Journal of Computational and Graphical Statistics, 2012, 21, 4, 940

    CrossRef

  93. 93
    Edward L. Ionides, Comment: Cell Motility Models and Inference for Dynamic Systems, Journal of the American Statistical Association, 2012, 107, 499, 865

    CrossRef

  94. You have free access to this content94
    Paul Fearnhead, Dennis Prangle, Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2012, 74, 3
  95. 95
    Ramiro Ruiz-Cárdenas, Elias T. Krainski, Håvard Rue, Direct fitting of dynamic models using integrated nested Laplace approximations — INLA, Computational Statistics & Data Analysis, 2012, 56, 6, 1808

    CrossRef

  96. 96
    Mark Kostuk, Bryan A. Toth, C. Daniel Meliza, Daniel Margoliash, Henry D. I. Abarbanel, Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods, Biological Cybernetics, 2012, 106, 3, 155

    CrossRef

  97. 97
    Hamid Moradkhani, Caleb M. DeChant, Soroosh Sorooshian, Evolution of ensemble data assimilation for uncertainty quantification using the particle filter-Markov chain Monte Carlo method, Water Resources Research, 2012, 48, 12
  98. 98
    Jonas Knape, Perry de Valpine, Fitting complex population models by combining particle filters with Markov chain Monte Carlo, Ecology, 2012, 93, 2, 256

    CrossRef

  99. 99
    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

  100. 100
    Michael K. Pitt, Ralph dos Santos Silva, Paolo Giordani, Robert Kohn, On some properties of Markov chain Monte Carlo simulation methods based on the particle filter, Journal of Econometrics, 2012, 171, 2, 134

    CrossRef

  101. 101
    Glenn Marion, Greg J. McInerny, Jörn Pagel, Stephen Catterall, Alex R. Cook, Florian Hartig, Robert B. O'Hara, Parameter and uncertainty estimation for process-oriented population and distribution models: data, statistics and the niche, Journal of Biogeography, 2012, 39, 12
  102. 102
    A. Bouchard-Cote, S. Sankararaman, M. I. Jordan, Phylogenetic Inference via Sequential Monte Carlo, Systematic Biology, 2012, 61, 4, 579

    CrossRef

  103. 103
    Christopher F. H. Nam, John A. D. Aston, Adam M. Johansen, Quantifying the uncertainty in change points, Journal of Time Series Analysis, 2012, 33, 5
  104. 104
    Meng Gao, Hui Zhang, Sequential Monte Carlo methods for parameter estimation in nonlinear state-space models, Computers & Geosciences, 2012, 44, 70

    CrossRef

  105. 105
    Michel Crucifix, Traditional and novel approaches to palaeoclimate modelling, Quaternary Science Reviews, 2012, 57, 1

    CrossRef

  106. 106
    Daniel J. Lawson, Grietje Holtrop, Harry Flint, Bayesian analysis of non-linear differential equation models with application to a gut microbial ecosystem, Biometrical Journal, 2011, 53, 4
  107. 107
    Thomas Flury, Neil Shephard, BAYESIAN INFERENCE BASED ONLY ON SIMULATED LIKELIHOOD: PARTICLE FILTER ANALYSIS OF DYNAMIC ECONOMIC MODELS, Econometric Theory, 2011, 27, 05, 933

    CrossRef

  108. 108
    Mingjun Zhong, Mark Girolami, Karen Faulds, Duncan Graham, Bayesian methods to detect dye-labelled DNA oligonucleotides in multiplexed Raman spectra, Journal of the Royal Statistical Society: Series C (Applied Statistics), 2011, 60, 2
  109. 109
    Emily Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky, Bayesian Nonparametric Inference of Switching Dynamic Linear Models, IEEE Transactions on Signal Processing, 2011, 59, 4, 1569

    CrossRef

  110. 110
    A. Golightly, D. J. Wilkinson, Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo, Interface Focus, 2011, 1, 6, 807

    CrossRef

  111. 111
    Carles Bretó, Edward L. Ionides, Compound Markov counting processes and their applications to modeling infinitesimally over-dispersed systems, Stochastic Processes and their Applications, 2011, 121, 11, 2571

    CrossRef

  112. 112
    Michael Dowd, Ruth Joy, Estimating behavioral parameters in animal movement models using a state-augmented particle filter, Ecology, 2011, 92, 3, 568

    CrossRef

  113. 113
    Michael Dowd, Estimating parameters for a stochastic dynamic marine ecological system, Environmetrics, 2011, 22, 4
  114. 114
    Anindya Bhadra, Edward L. Ionides, Karina Laneri, Mercedes Pascual, Menno Bouma, Ramesh C. Dhiman, Malaria in Northwest India: Data Analysis via Partially Observed Stochastic Differential Equation Models Driven by Lévy Noise, Journal of the American Statistical Association, 2011, 106, 494, 440

    CrossRef

  115. 115
    Martin M. Andreasen, Non-linear DSGE models and the optimized central difference particle filter, Journal of Economic Dynamics and Control, 2011, 35, 10, 1671

    CrossRef

  116. 116
    J. P. Nilmeier, G. E. Crooks, D. D. L. Minh, J. D. Chodera, Nonequilibrium candidate Monte Carlo is an efficient tool for equilibrium simulation, Proceedings of the National Academy of Sciences, 2011, 108, 45, E1009

    CrossRef

  117. 117
    GEIR STORVIK, On the Flexibility of Metropolis–Hastings Acceptance Probabilities in Auxiliary Variable Proposal Generation, Scandinavian Journal of Statistics, 2011, 38, 2
  118. 118
    Hedibert F. Lopes, Ruey S. Tsay, Particle filters and Bayesian inference in financial econometrics, Journal of Forecasting, 2011, 30, 1
  119. 119
    J.P. de Villiers, S.J. Godsill, S.S. Singh, Particle predictive control, Journal of Statistical Planning and Inference, 2011, 141, 5, 1753

    CrossRef

  120. 120
    Christopher K. Wikle, Scott H. Holan, Polynomial nonlinear spatio-temporal integro-difference equation models, Journal of Time Series Analysis, 2011, 32, 4
  121. You have free access to this content121
    Mark Girolami, Ben Calderhead, Riemann manifold Langevin and Hamiltonian Monte Carlo methods, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2011, 73, 2
  122. 122
    Bin Zhu, Jeremy M. G. Taylor, Peter X.-K. Song, Semiparametric Stochastic Modeling of the Rate Function in Longitudinal Studies, Journal of the American Statistical Association, 2011, 106, 496, 1485

    CrossRef

  123. 123
    Junye Li, Sequential Bayesian Analysis of Time-Changed Infinite Activity Derivatives Pricing Models, Journal of Business & Economic Statistics, 2011, 29, 4, 468

    CrossRef

  124. 124
    P. Jacob, C. P. Robert, M. H. Smith, Using Parallel Computation to Improve Independent Metropolis–Hastings Based Estimation, Journal of Computational and Graphical Statistics, 2011, 20, 3, 616

    CrossRef

  125. 125
    Paul Fearnhead, Handbook of Markov Chain Monte Carlo, 2011,

    CrossRef

  126. 126
    Anthony Lee, Christopher Yau, Michael B. Giles, Arnaud Doucet, Christopher C. Holmes, On the Utility of Graphics Cards to Perform Massively Parallel Simulation of Advanced Monte Carlo Methods, Journal of Computational and Graphical Statistics, 2010, 19, 4, 769

    CrossRef

  127. 127
    Paul Fearnhead, Omiros Papaspiliopoulos, Gareth O. Roberts, Andrew Stuart, Random-weight particle filtering of continuous time processes, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2010, 72, 4
  128. 128
    Christian P. Robert, Jean-Michel Marin, Judith Rousseau, Bayesian Inference and Computation,
  129. 129
    Michael Dowd, Dynamic Modeling, Encyclopedia of Environmetrics,
  130. 130
    Michael Dowd, Dynamic Modeling: Introduction, Wiley StatsRef: Statistics Reference Online,
  131. 131
    John F. Shortle, Pierre L'Ecuyer, Introduction to Rare-Event Simulation, Wiley Encyclopedia of Operations Research and Management Science,
  132. 132
    Jeong E. Lee, Kerrie L. Mengersen, Christian P. Robert, Issues in Designing Hybrid Algorithms,
  133. 133
    References,
  134. 134
    Sequential Importance Sampling Particle Filters,
  135. 135
    Darren J. Wilkinson, Stochastic Dynamical Systems,