C. Andrieu, A. Doucet, and R. Holenstein, 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 : 10.1111/j.1467-9868.2009.00736.x

A. Bouchard-côté, S. Sankararaman, and M. Jordan, Phylogenetic Inference via Sequential Monte Carlo, Systematic Biology, vol.61, issue.4, pp.579-593, 2012.
DOI : 10.1093/sysbio/syr131

R. Boys, D. Wilkinson, and T. Kirkwood, Bayesian inference for a discretely observed stochastic kinetic model, Statistics and Computing, vol.279, issue.3, pp.125-135, 2008.
DOI : 10.1007/s11222-007-9043-x

S. Buckland, K. Newman, C. Fernandez, L. Thomas, and J. Harwood, Embedding Population Dynamics Models in Inference, Statistical Science, vol.22, issue.1, pp.44-58, 2007.
DOI : 10.1214/088342306000000673

S. Cahon, N. Melab, and E. Talbi, ParadisEO: A Framework for the Reusable Design of Parallel and Distributed Metaheuristics, Journal of Heuristics, vol.10, issue.3, pp.357-380, 2004.
DOI : 10.1023/B:HEUR.0000026900.92269.ec

O. Cappé, E. Moulines, and T. Ryden, Inference in Hidden Markov Models, 2005.

O. Cappé and C. Robert, Markov Chain Monte Carlo: 10 years and Still Running!, Journal of the American Statistical Association, vol.95, pp.1282-1286, 2000.

F. Caron, M. Davy, A. Doucet, E. Duflos, and P. Vanheeghe, Bayesian Inference for Linear Dynamic Models With Dirichlet Process Mixtures, IEEE Transactions on Signal Processing, vol.56, issue.1, pp.71-84, 2008.
DOI : 10.1109/TSP.2007.900167
URL : https://hal.archives-ouvertes.fr/inria-00129646

F. Caron, M. Davy, E. Duflos, and P. Vanheeghe, Particle Filtering for Multisensor Data Fusion With Switching Observation Models: Application to Land Vehicle Positioning, IEEE Transactions on Signal Processing, vol.55, issue.6, pp.55-2703, 2007.
DOI : 10.1109/TSP.2007.893914

F. Caron, A. Doucet, and R. Gottardo, On-line changepoint detection and parameter estimation with application to genomic data, Statistics and Computing, vol.43, issue.1, pp.579-595, 2012.
DOI : 10.1007/s11222-011-9248-x
URL : https://hal.archives-ouvertes.fr/inria-00577217

C. Carvalho and H. Lopes, Simulation-based sequential analysis of Markov switching stochastic volatility models, Computational Statistics & Data Analysis, vol.51, issue.9, pp.4526-4542, 2007.
DOI : 10.1016/j.csda.2006.07.019

S. Cauchemez, A. Valleron, P. Boelle, . Flahault, and N. Ferguson, Estimating the impact of school closure on influenza transmission from Sentinel data, Nature, vol.54, issue.7188, pp.452-750, 2008.
DOI : 10.1038/nature06732

P. Collet, E. Lutton, M. Schoenauer, and J. Louchet, Take It EASEA, Parallel Problem Solving from Nature PPSN VI, pp.891-901, 2000.
DOI : 10.1007/3-540-45356-3_87
URL : https://hal.archives-ouvertes.fr/inria-00000875

D. Moral and P. , Feynman-Kac Formulae. Genealogical and Interacting Particle Systems with Application, 2004.
URL : https://hal.archives-ouvertes.fr/inria-00410165

D. Moral and P. , Mean Field Simulation for Monte Carlo Integration, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00932211

D. Moral, P. Peters, G. Vergé, and C. , An Introduction to Stochastic Particle Integration Methods: With Applications to Risk and Insurance, Monte Carlo and Quasi-Monte Carlo Methods, 2012.
DOI : 10.1007/978-3-642-41095-6_3
URL : https://hal.archives-ouvertes.fr/hal-01079281

S. Donnet and A. Samson, EM algorithm coupled with particle filter for maximum likelihood parameter estimation of stochastic differential mixed-effects models, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00519576

R. Douc, E. Moulines, and D. Stoffer, Nonlinear Time Series, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01263245

A. Doucet, N. De-freitas, and N. Gordon, Sequential Monte Carlo Methods in Practice, 2001.
DOI : 10.1007/978-1-4757-3437-9

A. Doucet, S. Godsill, and C. Andrieu, On Sequential Monte Carlo Sampling Methods for Bayesian Filtering, Statistics and Computing, vol.10, issue.3, pp.197-208, 2000.
DOI : 10.1023/A:1008935410038

A. Doucet and A. Johansen, A Tutorial on Particle Filtering and Smoothing: Fifteen Years Later Oxford Handbook of Nonlinear Filtering, 2011.

J. Dureau, K. Kalogeropoulos, and M. Baguelin, Capturing the time-varying drivers of an epidemic using stochastic dynamical systems, Biostatistics, vol.14, issue.3, p.52, 2013.
DOI : 10.1093/biostatistics/kxs052

D. Eddelbuettel, Seamless R and C++ Integration with Rcpp, 2013.
DOI : 10.1007/978-1-4614-6868-4

D. Eddelbuettel and R. François, Rcpp: Seamless R and C++ Integration, Journal of Statistical Software, vol.40, issue.8, pp.1-18, 2011.
DOI : 10.1007/978-1-4614-6868-4

D. Eddelbuettel and A. Johansen, RcppSMC: Rcpp bindings for Sequential Monte Carlo, 2014.

P. Fearnhead, Particle filters for mixture models with an unknown number of components, Statistics and Computing, vol.14, issue.1, pp.11-21, 2004.
DOI : 10.1023/B:STCO.0000009418.04621.cd

P. Fearnhead and Z. Liu, On-line inference for multiple changepoint problems, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.20, issue.4, pp.589-605, 2007.
DOI : 10.2307/2986119

J. Fernández-villaverde and J. Rubio-ramírez, Estimating Macroeconomic Models: A Likelihood Approach, Review of Economic Studies, vol.74, issue.4, pp.1059-1087, 2007.
DOI : 10.1111/j.1467-937X.2007.00437.x

T. Flury and N. Shephard, BAYESIAN INFERENCE BASED ONLY ON SIMULATED LIKELIHOOD: PARTICLE FILTER ANALYSIS OF DYNAMIC ECONOMIC MODELS, Econometric Theory, vol.140, issue.05, pp.933-956, 2011.
DOI : 10.1016/S0165-1889(03)00043-5

W. Gilks, S. Richardson, and D. Spiegelhalter, Markov Chain Monte Carlo in Practice, 1995.

W. Gilks, A. Thomas, and D. Spiegelhalter, A Language and Program for Complex Bayesian Modelling, The Statistician, vol.43, issue.1, pp.169-177, 1994.
DOI : 10.2307/2348941

D. Gillespie, Exact stochastic simulation of coupled chemical reactions, The Journal of Physical Chemistry, vol.81, issue.25, pp.2340-2361, 1977.
DOI : 10.1021/j100540a008

A. Golightly and C. Gillespie, Simulation of Stochastic Kinetic Models, In In Silico Systems Biology, pp.169-187, 2013.
DOI : 10.1007/978-1-62703-450-0_9

A. Golightly and D. Wilkinson, Bayesian Sequential Inference for Stochastic Kinetic Biochemical Network Models, Journal of Computational Biology, vol.13, issue.3, pp.838-851, 2006.
DOI : 10.1089/cmb.2006.13.838

A. Golightly and D. Wilkinson, Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo, Interface Focus, vol.149, issue.3, p.20110047, 2011.
DOI : 10.1111/j.1541-0420.2005.00345.x

P. Green and N. Hjort, Highly Structured Stochastic Systems, 2003.

F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell, J. Jansson et al., Particle filters for positioning, navigation, and tracking, IEEE Transactions on Signal Processing, vol.50, issue.2, pp.425-437, 2002.
DOI : 10.1109/78.978396

A. Johansen, SMCTC: Sequential Monte Carlo in C++, Journal of Statistical Software, vol.30, pp.1-41, 2009.

M. Jordan, Graphical Models, Statistical Science, vol.19, issue.1, pp.140-155, 2004.
DOI : 10.1214/088342304000000026

S. Lauritzen, Graphical Models, 1996.

A. Lee, C. Yau, M. Giles, A. Doucet, and C. Holmes, On the Utility of Graphics Cards to Perform Massively Parallel Simulation of Advanced Monte Carlo Methods, Journal of Computational and Graphical Statistics, vol.19, issue.4, pp.769-789, 2010.
DOI : 10.1198/jcgs.2010.10039

J. Liu, Monte Carlo Strategies in Scientific Computing, 2001.
DOI : 10.1007/978-0-387-76371-2

D. Lunn, C. Jackson, N. Best, A. Thomas, and D. Spiegelhalter, The BUGS Book: A Practical Introduction to Bayesian Analysis, 2012.

D. Lunn, A. Thomas, N. Best, and D. Spiegelhalter, WinBUGS -a Bayesian Modelling Framework: Concepts, Structure and Extensibility, Statistics and Computing, vol.10, issue.4, pp.325-337, 2000.
DOI : 10.1023/A:1008929526011

V. Mansinghka, D. Selsam, and Y. Perov, Venture: A Higher-order Probabilistic Programming Platform with Programmable Inference, 2014.

L. Murray, Bayesian State-Space Modelling on High-Performance Hardware Using LibBi, 2013.

C. Naesseth, F. Lindsten, and T. Schön, Sequential Monte Carlo for Graphical Models, Advances in Neural Information Processing Systems (NIPS), 2014.

G. Peters, G. Hosack, and K. Hayes, Ecological non-linear state space model selection via adaptive particle Markov chain Monte Carlo (AdPMCMC), 2010.

M. Pitt and N. Shephard, Filtering via Simulation: Auxiliary Particle Filters, Journal of the American Statistical Association, vol.24, issue.446, pp.590-599, 1999.
DOI : 10.1016/0005-1098(71)90097-5

M. Plummer, JAGS: A Program for Analysis of Bayesian Graphical Models using Gibbs Sampling, Proceedings of the 3rd International Workshop on Distributed Statistical Computing, 2003.

M. Plummer, JAGS Version 3.3.0 user manual, 2012.

M. Plummer, rjags: Bayesian graphical models using MCMC. R package version 3-13, 2014.

B. Ristic, S. Arulampalam, and N. Gordon, Beyond the Kalman filter: Particle filters for tracking applications, 2004.

C. Robert and G. Casella, Monte Carlo Statistical Methods, 2004.

S. Development and T. , Stan: A C++ Library for Probability and Sampling, Version 2.1, 2013.

S. Thrun, D. Fox, W. Burgard, and F. Dellaert, Robust Monte Carlo localization for mobile robots, Artificial Intelligence, vol.128, issue.1-2, pp.99-141, 2001.
DOI : 10.1016/S0004-3702(01)00069-8

C. Vergé, C. Dubarry, D. Moral, P. Moulines, and E. , On parallel implementation of sequential Monte Carlo methods: the island particle model, Statistics and Computing, vol.90, issue.420, 2013.
DOI : 10.1007/s11222-013-9429-x

J. Vermaak, C. Andrieu, A. Doucet, and S. Godsill, Particle methods for Bayesian modeling and enhancement of speech signals, IEEE Transactions on Speech and Audio Processing, vol.10, issue.3, pp.173-185, 2002.
DOI : 10.1109/TSA.2002.1001982

B. Vo, S. Singh, and A. Doucet, Sequential Monte Carlo implementation of the PHD filter for multi-target tracking, Proc. International Conference on Information Fusion, pp.792-799, 2003.

F. Wood, J. Van-de-meent, and V. Mansinghka, A New Approach to Probabilistic Programming Inference, Proceedings of the 17th International conference on Artificial Intelligence and Statistics, 2014.

A. Todeschini and I. Sud, Ouest 200 avenue de la vieille tour 33405 Talence Cedex, France E-mail: Adrien.Todeschini@inria.fr URL: https