Feynman-Kac measures and Interacting Particle systems :

Selected studies on : Advanced Monte Carlo Algorithms

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• Software BiiPS

  
  The software BIIPS is a general software developed by the INRIA team ALEA for bayesian inference with interacting particle systems, a.k.a. Sequential Monte Carlo methods. A demonstration of the BiiPS software for estimating the stochastic volatility of financial data can be found in
  
  
  • BIIPS : Bayesian inference with interacting Particle Systems in Finance,
    
  
  
  
  INRIA project manager : F. Caron, Design and developement engineer : A. Todeschini, Project contributors : P. Del Moral & P. Legrand, Logo designer : Timothée Del Moral.
  
  
  
  
  
  
  
  
  

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• Projects homepages

  
  
  • Advanced Learning Evolutionary Algorithms (ALEA team project, Centre INRIA Bordeaux Sud-Ouest)
    
    
  • Sequential Monte Carlo Methods & Particle Filters Resources (Arnaud Doucet , Department of Statistics, Oxford University)
    
    
  • Applications of Interacting Particle Systems to Statistics (IRISA, INRIA Rennes)
    
    
  • Sequential Learning (SEQUEL project, INRIA Lille)
    
    
  • Sequential Monte Carlo homepage, particle filtering (Elena Punskaya , Department of Engineering, Cambridge University)
    
    
    
    
    
    
    
    
    
    
    
    

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• Recent tutorials & Surveys

  
  
  • Pierre Del Moral, Peng Hu, Liming Wu
    On the concentration properties of Interacting particle processes HAL-INRIA RR-7677 (2011),
    Foundations and Trends in Machine Learning, Vol. 3, No. 3 - 4, 225-389 (2012). (online article, 167 pages)
    
    
  • Particle Methods: An introduction with applications (Pierre Del Moral, Arnaud Doucet) HAL-INRIA RR-6991 [50p] (2009).
    2008 Machine Learning Summer School, Springer LNCS/LNAI Tutorial book no. 6368 (2010-2011).
    
    
  • Branching and Interacting Particle Systems Approximations of Feynman-Kac Formulae with Applications to Non-Linear Filtering
    (Del Moral P., Miclo L.) Séminaire de Probabilités XXXIV, Ed. J. Azéma and M. Emery and M. Ledoux and M. Yor.
    Lecture Notes in Mathematics, Springer-Verlag Berlin, Vol. 1729, 1-145 (2000).
    
    
  • A Mean Field Theory of Nonlinear Filtering (P. Del Moral, F. Patras, & S. Rubenthaler)
    the Oxford Handbook of Nonlinear Filtering, Oxford University Press, 2011)
    
    
  • An overview of existing methods and recent advances in sequential Monte Carlo (Olivier Cappe, Simon J. Godsill and Eric Moulines)
    
    
  A series of recent lectures presenting this interacting particle methodology in a more general framework are provided below
  • Sino-French Summer Institute 2011.Beijing, China, June 13-25, 2011. ((lecture note 1), (lecture note 2), and (lecture note 3))
    
  • INRIA Workshop on Statistical Learning Institut Henri Poincaré Paris, December 5-6, 2011 ( (lecture notes) )
  • University of Cambridge, Statistics Laboratory seminar, Feb. 2010. (lecture notes)
    
  • 10th Machine Learning summer school, September 1-15th, 2008. (lecture notes)
  • Opening workshop, Sequential Monte Carlo Methods, SAMSI, Duke Univ. September 7-10th, 2008. (lecture notes)
    
    
    
    

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• Sequential Monte Carlo methodologies

  
  
  • Sequential Monte Carlo Samplers (Del Moral P., Doucet A., Jasra A) Journal of the Royal Society of Statistics, Series B. vol. 68, No. 3, pp. 411-436 (2006).
    
    
  • Particle Markov chain Monte Carlo methods (Ch. Andrieu, Arnaud Doucet, R. Holenstein , J. R. Statist. Soc. B (2010))
    
    
  • The Gibbs Cloner for Combinatorial Optimization, Counting and Sampling (R. Rubinstein, Technion 2008)
    
    
  • Parameter Estimation and Asymptotic Stability in Stochastic Filtering (A. Papavasiliou, Warwick Univ.)
    
    
  • Randomized Algorithms with Splitting: Why the Classic Randomized Algorithms do not Work and how to Make them Work (R. Rubinstein, Technion 2009)
    
    
  • Exact distributions and Sequential Monte Carlo for Change Point Analysis (Christopher F. H. Nam, John A. D. Aston and Adam M. Johansen, University of Warwick, 2010)
    
    
  • Interacting Multiple Try Algorithms with Different Proposal Distributions (Roberto Casarin, Radu V. Craiu, Fabrizio Leisen, 2010)
    
    
  • Interacting Sequential Monte Carlo Samplers for Trans-dimensional Simulation (Ajay Jasra, Arnaud Doucet, David A. Stephens, and Christopher C. Holmes, Univ. Oxford and Imperial College)
    
    
  • On population-based simulation for static inference (Ajay Jasra, David A. Stephens, and Christopher C. Holmes, Univ. Oxford and Imperial College, 2007)
    
    
  • Antithetic sampling for sequential Monte Carlo methods, with application to state space models (S. Bizjajeva, and J. Olsson)
    
    
  • Sequential Monte Carlo samplers for Bayesian DSGE models (Drew Creal, Department of Econometrics and Operations Research, Amsterdam 2007)
    
    
  • Sequential Monte Carlo Sampling in Hidden Markov Models of Nonlinear Dynamical Systems (X. Zeng, M. Anitescu, Argonne National Laboratory 2011)
    
    
  • Monte Carlo Methods (Adam M. Johansen and Ludger Evers, Bristol University, Lecture Notes 2007)
    
    
  • Sequential Monte Carlo Simulated Annealing.(Enlu Zhou Xi Chen, Illinois Univ. 2011)
    
    
  • Fix Lag Sequential Monte Carlo (A. Doucet, S. Senecal; Cambridge Univ. and The Institute of Statistical Mathematics Tokyo)
    
    
  • Sequential Monte Carlo without likelihoods (A.M. Johansen, A. Doucet, M. Davy, 2007)
    
    
  • On Sequential Monte Carlo Sampling of Discretely Observed Stochastic Differential Equations (Simo Sarkka, Helsinki Univ.)
    
    
  • On Adaptive Resampling Procedures for Sequential Monte Carlo Methods(Del Moral P., Doucet A., Jasra A) HAL-INRIA RR-6700 [46p], (Oct. 2008). To appear in Bernoulli (2011-2012).
    
    
  • An Adaptive Sequential Monte Carlo Method for Approximate Bayesian Computation(Pierre Del Moral, Arnaud Doucet , Ajay Jasra) to appear in Statistics and Computing (2011-2012)
    
    
  • On sequential Monte Carlo, partial rejection control and approximate Bayesian computation (G. Peters, Y. Fan, S.A. Sisson, 2009)
    
    
  • Sequential Monte Carlo Methods for Bayesian Filtering (Ph.D. L.C. Sing Gilbert, Hong-Kong Univ. 2008)
    
    
  • Efficient Bayesian Inference for Switching State-Space Models using Particle Markov chain Monte Carlo methods. (N. Whiteley A. Doucet, Chr. Andrieu. Bristol Statistics Research Report)
    
    
  • A One Pass Sequential Monte Carlo Method for Bayesian Analysis of Massive Datasets (S. Balakrishnan, D. Madigan; Rudgers Univ.)
    
    

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• Interacting Markov chain Monte Carlo models

  
  
  • Sequential Monte Carlo Samplers (Del Moral P., Doucet A., Jasra A) Journal of the Royal Society of Statistics, Series B. vol. 68, No. 3, pp. 411-436 (2006).
    
    
  • Sequentially Interacting Markov Chain Monte Carlo (A. Doucet, A.E. Brockwell; UBC and Carnegie Mellon Univ.)
    
    
  • An Introduction to Interacting Simulated Annealing (Juergen Gall, Bodo Rosenhahn, and Hans-Peter Seidel, Max-Planck Institute for Computer Science)
    
    
  • Interacting Markov Chain Monte Carlo Methods For Solving Nonlinear Measure-Valued Equations
    (HAL-INRIA RR-6435 (2008)) P. Del Moral, A. Doucet The Annals of Applied Probability, Vol. 20, No. 2, 593-639 (2010)
    
    
  • A Functional Central Limit Theorem for a Class of Interacting Markov Chain Monte Carlo Models (HAL-INRIA RR-6436 (2008))
    B. Bercu, P. Del Moral, A. Doucet Electronic Journal of Probability. Vol. 14, Paper no. 73, pages 2130-2155 (2009).
    
    
  • Fluctuations of Interacting Markov Chain Monte Carlo Models
    B. Bercu, P. Del Moral, A. Doucet HAL-INRIA RR-6438 [28p], [submitted] (Feb. 2008).
    
    
    
    
    
    
    

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• Some theoretical analysis

  
  
  • Nonlinear Filtering : Interacting Particle Resolution.
    (Del Moral P., Markov Processes and Related Fields, vol. 2, no; 4, pp. 555-580, 1996)
    
    
  • Branching and Interacting Particle Systems Approximations of Feynman-Kac Formulae with Applications to Non-Linear Filtering
    (Del Moral P., Miclo L.) Séminaire de Probabilités XXXIV, Ed. J. Azéma and M. Emery and M. Ledoux and M. Yor. Lecture Notes in Mathematics, Springer-Verlag Berlin, Vol. 1729, 1-145 (2000).
    
    
  • On the stability of interacting processes with applications to filtering and genetic algorithms (Del Moral P., Guionnet A.) Annales de l'Institut Henri Poincaré, Vol. 37, No. 2, 155-194 (2001).
    
    
  • Feynman-Kac Formulae, Genealogical and Interacting Particle Systems with Applications Del Moral P., Springer New York; Springer Heidelberg, Series: Probability and its Applications, [585 pages] (April 2004).
    
    
  • Sequential Monte Carlo samplers: error bounds and insensitivity to initial conditions. 2011. (Nick Whiteley, School of Mathematics, Bristol Univ.)
    
    
  • Stability of Sequential Markov Chain Monte Carlo Methods (A. Eberle, C. Marinelli, Bonn Univ.)
    
    
  • On the Stability of Sequential Monte Carlo Methods in High Dimensions.(Alexandros Beskos, Dan Crisan, and Ajay Jasra, Imperial College 2011)
    
    
  
  
  
  
  

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