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16. A. Beretti and A. Sokal. New Monte Carlo method for the self-avoiding
walk. J. Statist. Phys., vol. 40, pp. 483–531 (1985).
17. B. A. Berg. Markov Chain Monte Carlo Simulations and Their Statistical
Analysis. Singapore, World Scientific (2004).
18. D. Bohm. La pl´ enitude de l’Univers, Seveyrat (1989).
19. W. M. Bolstad. Understanding Computational Bayesian Statistics, John Wiley & Sons (2010)
20. J. F. Bonnans, J. Ch. Gilbert, C. Lemar´ echal, C. Sagastiz` abal. Numerical Optimization - Theoretical and Practical Aspects (2006).
21. M. Bossy, N. Champagnat. Markov processes and parabolic partial differential equations. Encyclopedia of Quantitative Finance, Cont Rama (Ed.) pp.
1142–1159 (2010) .
22. M. Bossy, D. Talay. Convergence rate for the approximation of the limit law
of weakly interacting particles : application to the Burgers equation. Ann. Appl.
Probab. vol. 6, no. 3, pp. 818–861 (1996).
23. S. Branford, C. Weihrauch and V. Alexandrov. A sparse parallel hybrid
Monte Carlo algorithms for matrix computations, Proceedings of the 2005 International Conference on Computational Science, Eds. : V. Sunderam et al,
Lecture Note in Computer Science, Vol. 3516, pp. 743-751 (2005).
24. M. L. Bujorianu, J. Lygeros. Reachability questions in piecewise deterministic Markov processes. In O. Maler and A. Pnueli, editors, Hybrid Systems :
Computation and Control, number 2623 in LNCS, pages 126 ?140. SpringerVerlag, Berlin (2003).
25. G. Burgers, P. Jan van Leeuwen, and G. Evensen. Analysis scheme in the
ensemble Kalman filter. Monthly Weather Review, vol. 126, no. 6, pp. 1719–1724
(1998).
26. O. Capp´ e, E. Moulines, and T. Ryd` en. Inference in Hidden Markov Models,
Springer-Verlag (2005).
27. F. C´ erou, P. Del Moral, A. Guyader. (2008). A non asymptotic variance
theorem for unnormalized Feynman-Kac particle models. Annales de l’Intitut
H. Poincar´ e (B) 47(3), (2011).
28. B. Davis. Reinforced random walk. Prob. Theory Related Fields, vol. 84, pp.
203–22 (1990).
29. B. Davis. Reinforced and perturbed random walks. In Random Walks, volume
9 of Bolyai Soc. Math. Stud., pages 113–126 (1999).
30. M. Davis. Piecewise-deterministic Markov processes : a general class of nondiffusion stochastic models. JRSS series B, vol. 46, no. 453 (1984)
31. M. Davis. Markov Models and Optimization. Monographs on Statistics and
Applied Probability, Chapman & Hall (1993).
32. Th. Dean, S.S. Singh, A. Jasra, G.W. Peters. Parameter estimation for hidden Markov models with intractable likelihoods. ArXiv preprint,
arXiv :1103.5399v1 (2011).
33. J. P. Dedieu. Points Fixes, Z´ eros et la M´ ethodes de Newton. Math´ ematiques
et Applications 54. Springer Verlag, Berlin (2006).
Litt´ erature
16. A. Beretti and A. Sokal. New Monte Carlo method for the self-avoiding
walk. J. Statist. Phys., vol. 40, pp. 483–531 (1985).
17. B. A. Berg. Markov Chain Monte Carlo Simulations and Their Statistical
Analysis. Singapore, World Scientific (2004).
18. D. Bohm. La pl´ enitude de l’Univers, Seveyrat (1989).
19. W. M. Bolstad. Understanding Computational Bayesian Statistics, John Wiley & Sons (2010)
20. J. F. Bonnans, J. Ch. Gilbert, C. Lemar´ echal, C. Sagastiz` abal. Numerical Optimization - Theoretical and Practical Aspects (2006).
21. M. Bossy, N. Champagnat. Markov processes and parabolic partial differential equations. Encyclopedia of Quantitative Finance, Cont Rama (Ed.) pp.
1142–1159 (2010) .
22. M. Bossy, D. Talay. Convergence rate for the approximation of the limit law
of weakly interacting particles : application to the Burgers equation. Ann. Appl.
Probab. vol. 6, no. 3, pp. 818–861 (1996).
23. S. Branford, C. Weihrauch and V. Alexandrov. A sparse parallel hybrid
Monte Carlo algorithms for matrix computations, Proceedings of the 2005 International Conference on Computational Science, Eds. : V. Sunderam et al,
Lecture Note in Computer Science, Vol. 3516, pp. 743-751 (2005).
24. M. L. Bujorianu, J. Lygeros. Reachability questions in piecewise deterministic Markov processes. In O. Maler and A. Pnueli, editors, Hybrid Systems :
Computation and Control, number 2623 in LNCS, pages 126 ?140. SpringerVerlag, Berlin (2003).
25. G. Burgers, P. Jan van Leeuwen, and G. Evensen. Analysis scheme in the
ensemble Kalman filter. Monthly Weather Review, vol. 126, no. 6, pp. 1719–1724
(1998).
26. O. Capp´ e, E. Moulines, and T. Ryd` en. Inference in Hidden Markov Models,
Springer-Verlag (2005).
27. F. C´ erou, P. Del Moral, A. Guyader. (2008). A non asymptotic variance
theorem for unnormalized Feynman-Kac particle models. Annales de l’Intitut
H. Poincar´ e (B) 47(3), (2011).
28. B. Davis. Reinforced random walk. Prob. Theory Related Fields, vol. 84, pp.
203–22 (1990).
29. B. Davis. Reinforced and perturbed random walks. In Random Walks, volume
9 of Bolyai Soc. Math. Stud., pages 113–126 (1999).
30. M. Davis. Piecewise-deterministic Markov processes : a general class of nondiffusion stochastic models. JRSS series B, vol. 46, no. 453 (1984)
31. M. Davis. Markov Models and Optimization. Monographs on Statistics and
Applied Probability, Chapman & Hall (1993).
32. Th. Dean, S.S. Singh, A. Jasra, G.W. Peters. Parameter estimation for hidden Markov models with intractable likelihoods. ArXiv preprint,
arXiv :1103.5399v1 (2011).
33. J. P. Dedieu. Points Fixes, Z´ eros et la M´ ethodes de Newton. Math´ ematiques
et Applications 54. Springer Verlag, Berlin (2006).