, The pseudo-marginal approach for efficient Monte Carlo computations . The Annals of Statistics, pp.697-725, 2009.
, Particle Markov chain Monte Carlo methods, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.50, issue.3, pp.269-342, 2010.
DOI : 10.1214/06-BA127
URL : http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9868.2009.00736.x/pdf
, Estimation of population growth or decline in genetically monitored populations, Genetics, vol.164, issue.3, pp.1139-1160, 2003.
, Inference in hidden Markov models, 2005.
, An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo, Proceedings of the IEEE, pp.899-924, 2007.
DOI : 10.1109/JPROC.2007.893250
, Convergence of a stochastic approximation version of the EM algorithm, Annals of Statistics, pp.94-128, 1999.
, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, Series B, vol.39, issue.1, pp.1-38, 1977.
, Estimation in the partially observed stochastic Morris???Lecar neuronal model with particle filter and stochastic approximation methods, The Annals of Applied Statistics, vol.8, issue.2, pp.674-702, 2014.
DOI : 10.1214/14-AOAS729
URL : https://hal.archives-ouvertes.fr/hal-00712331
, Parametric inference for mixed models defined by stochastic differential equations, ESAIM: Probability and Statistics, vol.80, pp.196-218, 2008.
DOI : 10.1093/biomet/80.4.791
URL : https://hal.archives-ouvertes.fr/hal-00263515
, Sequential Monte Carlo methods in practice, 2001.
DOI : 10.1007/978-1-4757-3437-9
, A Comparison of Inferential Methods for Highly Nonlinear State Space Models in Ecology and Epidemiology, Statistical Science, vol.31, issue.1, pp.96-118, 2016.
DOI : 10.1214/15-STS534SUPP
, Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo, Interface Focus, vol.149, issue.3, pp.807-820, 2011.
DOI : 10.1007/s11222-007-9045-8
URL : http://rsfs.royalsocietypublishing.org/content/royfocus/1/6/807.full.pdf
, Novel approach to nonlinear/non-Gaussian Bayesian state estimation, IEE Proceedings F-Radar and Signal Processing, pp.107-113, 1993.
DOI : 10.1049/ip-f-2.1993.0015
, Tempered particle filtering, National Bureau of Economic Research, 2017.
DOI : 10.17016/feds.2016.072
, Efficient Estimation of Detailed Single-Neuron Models, Journal of Neurophysiology, vol.96, issue.2, pp.872-890, 2006.
DOI : 10.1152/jn.00079.2006
URL : http://jn.physiology.org/content/jn/96/2/872.full.pdf
, Smoothing of, and Parameter Estimation from, Noisy Biophysical Recordings. PLOS Computational Biology, vol.5, issue.5, p.1000379, 2009.
, Inference for dynamic and latent variable models via iterated, perturbed Bayes maps, Proceedings of the National Academy of Sciences, pp.719-724, 2015.
DOI : 10.1109/JPROC.2007.893250
URL : http://www.pnas.org/content/112/3/719.full.pdf
, Filtering via approximate Bayesian computation, Statistics and Computing, vol.58, issue.6, pp.1223-1237, 2012.
DOI : 10.1093/oxfordjournals.molbev.a026091
, On Particle Methods for Parameter Estimation in State-Space Models, Statistical Science, vol.30, issue.3, pp.328-351, 2015.
DOI : 10.1214/14-STS511
, Statistical inference for partially observed Markov processes via the R package pomp, Journal of Statistical Software, issue.12, pp.69-2015
DOI : 10.18637/jss.v069.i12
URL : https://doi.org/10.18637/jss.v069.i12
, Monte Carlo filter and smoother for non-Gaussian nonlinear state space models, Journal of Computational and Graphical statistics, vol.5, issue.1, pp.1-25, 1996.
DOI : 10.2307/1390750
, Maximum likelihood estimation in nonlinear mixed effects models, Computational Statistics & Data Analysis, vol.49, issue.4, pp.1020-1038, 2005.
DOI : 10.1016/j.csda.2004.07.002
, Mixed effects models for the population approach: models, tasks, methods and tools, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01122873
, An efficient stochastic approximation EM algorithm using conditional particle filters, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing
DOI : 10.1109/ICASSP.2013.6638872
URL : http://users.isy.liu.se/en/rt/schon/Publications/LindstenSJ2013.pdf
, Monte Carlo strategies in scientific computing, 2008.
DOI : 10.1007/978-0-387-76371-2
, Finding the observed information matrix when using the EM algorithm, Journal of the Royal Statistical Society. Series B, pp.226-233, 1982.
, Approximate Bayesian computational methods, Statistics and Computing, vol.6, issue.31, pp.1167-1180, 2012.
DOI : 10.1098/rsif.2008.0172
URL : https://hal.archives-ouvertes.fr/hal-00567240
, Auxiliary likelihood-based approximate Bayesian computation in state space models, 2016.
, A General Framework for the Parametrization of Hierarchical Models, Statistical Science, vol.22, issue.1, pp.59-73, 2007.
DOI : 10.1214/088342307000000014
, Inference for SDE Models via Approximate Bayesian Computation, Journal of Computational and Graphical Statistics, vol.58, issue.4, pp.1080-1100, 2014.
DOI : 10.1098/rsif.2008.0172
URL : http://arxiv.org/pdf/1204.5459.pdf
, Accelerating inference for diffusions observed with measurement error and large sample sizes using approximate Bayesian computation, Journal of Statistical Computation and Simulation, vol.140, issue.2, pp.195-213, 2015.
DOI : 10.1002/jae.3950050202
URL : http://arxiv.org/pdf/1310.0973
, Approximations to the log-likelihood function in the nonlinear mixedeffects model, Journal of Computational and Graphical Statistics, vol.4, issue.1, pp.12-35, 1995.
, On the efficiency of pseudo-marginal random walk Metropolis algorithms. The Annals of Statistics, pp.238-275, 2015.
, Estimation of parameters and unobserved components for nonlinear systems from noisy time series, Physical Review E, vol.147, issue.1, p.16210, 2002.
DOI : 10.1016/S0167-2789(00)00166-4
, smfsb: Stochastic modelling for systems biology, 2015.