IDEAS home Printed from https://ideas.repec.org/r/nat/nature/v466y2010i7310d10.1038_nature09319.html
   My bibliography  Save this item

Statistical inference for noisy nonlinear ecological dynamic systems

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Forneron, Jean-Jacques & Ng, Serena, 2018. "The ABC of simulation estimation with auxiliary statistics," Journal of Econometrics, Elsevier, vol. 205(1), pages 112-139.
  2. Henri Pesonen & Umberto Simola & Alvaro Köhn‐Luque & Henri Vuollekoski & Xiaoran Lai & Arnoldo Frigessi & Samuel Kaski & David T. Frazier & Worapree Maneesoonthorn & Gael M. Martin & Jukka Corander, 2023. "ABC of the future," International Statistical Review, International Statistical Institute, vol. 91(2), pages 243-268, August.
  3. Barde, Sylvain, 2024. "Bayesian estimation of large-scale simulation models with Gaussian process regression surrogates," Computational Statistics & Data Analysis, Elsevier, vol. 196(C).
  4. Oliver J Maclaren & Aimée Parker & Carmen Pin & Simon R Carding & Alastair J M Watson & Alexander G Fletcher & Helen M Byrne & Philip K Maini, 2017. "A hierarchical Bayesian model for understanding the spatiotemporal dynamics of the intestinal epithelium," PLOS Computational Biology, Public Library of Science, vol. 13(7), pages 1-23, July.
  5. Joel Dyer & Patrick Cannon & J. Doyne Farmer & Sebastian Schmon, 2022. "Black-box Bayesian inference for economic agent-based models," Papers 2202.00625, arXiv.org.
  6. Aushev, Alexander & Pesonen, Henri & Heinonen, Markus & Corander, Jukka & Kaski, Samuel, 2022. "Likelihood-free inference with deep Gaussian processes," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
  7. Nguyen, Dao, 2016. "Another look at Bayes map iterated filtering," Statistics & Probability Letters, Elsevier, vol. 118(C), pages 32-36.
  8. Grazzini, Jakob & Richiardi, Matteo G. & Tsionas, Mike, 2017. "Bayesian estimation of agent-based models," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 26-47.
  9. Priddle, Jacob W. & Drovandi, Christopher, 2023. "Transformations in semi-parametric Bayesian synthetic likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
  10. Hojun You & Kyubaek Yoon & Wei-Ying Wu & Jongeun Choi & Chae Young Lim, 2024. "Regularized nonlinear regression with dependent errors and its application to a biomechanical model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 76(3), pages 481-510, June.
  11. George Karabatsos, 2018. "On Bayesian Testing of Additive Conjoint Measurement Axioms Using Synthetic Likelihood," Psychometrika, Springer;The Psychometric Society, vol. 83(2), pages 321-332, June.
  12. Anthony Ebert & Kerrie Mengersen & Fabrizio Ruggeri & Paul Wu, 2021. "Curve Registration of Functional Data for Approximate Bayesian Computation," Stats, MDPI, vol. 4(3), pages 1-14, September.
  13. Wanchuang Zhu & Yanan Fan, 2023. "A synthetic likelihood approach for intractable markov random fields," Computational Statistics, Springer, vol. 38(2), pages 749-777, June.
  14. Ramis Khabibullin & Sergei Seleznev, 2022. "Fast Estimation of Bayesian State Space Models Using Amortized Simulation-Based Inference," Bank of Russia Working Paper Series wps104, Bank of Russia.
  15. Niyousha Hosseinichimeh & Hazhir Rahmandad & Mohammad S. Jalali & Andrea K. Wittenborn, 2016. "Estimating the parameters of system dynamics models using indirect inference," System Dynamics Review, System Dynamics Society, vol. 32(2), pages 154-178, April.
  16. Jean-Jacques Forneron, 2019. "A Scrambled Method of Moments," Papers 1911.09128, arXiv.org.
  17. Ong, Victor M.-H. & Nott, David J. & Tran, Minh-Ngoc & Sisson, Scott A. & Drovandi, Christopher C., 2018. "Likelihood-free inference in high dimensions with synthetic likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 271-291.
  18. Wiqvist, Samuel & Golightly, Andrew & McLean, Ashleigh T. & Picchini, Umberto, 2021. "Efficient inference for stochastic differential equation mixed-effects models using correlated particle pseudo-marginal algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
  19. King, Aaron A. & Lin, Qianying & Ionides, Edward L., 2022. "Markov genealogy processes," Theoretical Population Biology, Elsevier, vol. 143(C), pages 77-91.
  20. Dehideniya, Mahasen B. & Drovandi, Christopher C. & McGree, James M., 2018. "Optimal Bayesian design for discriminating between models with intractable likelihoods in epidemiology," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 277-297.
  21. Jonathan U Harrison & Ruth E Baker, 2020. "An automatic adaptive method to combine summary statistics in approximate Bayesian computation," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-21, August.
  22. Gael M. Martin & David T. Frazier & Christian P. Robert, 2020. "Computing Bayes: Bayesian Computation from 1763 to the 21st Century," Monash Econometrics and Business Statistics Working Papers 14/20, Monash University, Department of Econometrics and Business Statistics.
  23. Gael M. Martin & David T. Frazier & Christian P. Robert, 2021. "Approximating Bayes in the 21st Century," Monash Econometrics and Business Statistics Working Papers 24/21, Monash University, Department of Econometrics and Business Statistics.
  24. Dyer, Joel & Cannon, Patrick & Farmer, J. Doyne & Schmon, Sebastian M., 2024. "Black-box Bayesian inference for agent-based models," Journal of Economic Dynamics and Control, Elsevier, vol. 161(C).
  25. Golchi, Shirin & Campbell, David A., 2016. "Sequentially Constrained Monte Carlo," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 98-113.
  26. Evrendilek, F. & Karakaya, N., 2014. "Regression model-based predictions of diel, diurnal and nocturnal dissolved oxygen dynamics after wavelet denoising of noisy time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 8-15.
  27. repec:plo:pone00:0072086 is not listed on IDEAS
  28. Barraquand, Frédéric & Gimenez, Olivier, 2019. "Integrating multiple data sources to fit matrix population models for interacting species," Ecological Modelling, Elsevier, vol. 411(C).
  29. Delli Gatti,Domenico & Fagiolo,Giorgio & Gallegati,Mauro & Richiardi,Matteo & Russo,Alberto (ed.), 2018. "Agent-Based Models in Economics," Cambridge Books, Cambridge University Press, number 9781108400046, June.
  30. Daniel Durstewitz, 2017. "A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements," PLOS Computational Biology, Public Library of Science, vol. 13(6), pages 1-33, June.
  31. Georgia Koppe & Hazem Toutounji & Peter Kirsch & Stefanie Lis & Daniel Durstewitz, 2019. "Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-35, August.
  32. Lili Zhuang & Noel Cressie, 2014. "Bayesian hierarchical statistical SIRS models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(4), pages 601-646, November.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.