Akashi H. and Kumamoto H. 1975. Construction of discrete-time nonlinear filter by Monte Carlo methods with variance-reducing techniques. Systems and Control 19: 211–221 (in Japanese).

Akashi H. and Kumamoto H. 1977. Random sampling approach to state estimation in switching environments. Automatica 13: 429–434.

Anderson B.D.O. and Moore J.B. 1979. Optimal Filtering. Englewood Cliffs.

Berzuini C., Best N., Gilks W., and Larizza C. 1997. Dynamic conditional independence models and markov chain Monte Carlo methods. Journal of the American Statistical Association 92: 1403–1412.

Billio M. and Monfort A. 1998. Switching state-space models: Likelihood function, filtering and smoothing. Journal of Statistical Planning and Inference 68: 65–103.

Carpenter J., Clifford P., and Fearnhead P. 1997. An improved particle filter for nonlinear problems. Technical Report, University of Oxford, Dept. of Statistics.

Casella G. and Robert C.P. 1996. Rao-Blackwellisation of sampling schemes. Biometrika 83: 81–94.

Chen R. and Liu J.S. 1996. Predictive updating methods with application to Bayesian classification. Journal of the Royal Statistical Society B 58: 397–415.

Clapp T.C. and Godsill S.J. 1999. Fixed-lag smoothing using sequential importance sampling. In: Bernardo J.M., Berger J.O., Dawid A.P., and Smith A.F.M. (Eds.), Bayesian Statistics, Vol. 6, Oxford University Press, pp. 743–752.

Doucet A. 1997. Monte Carlo methods for Bayesian estimation of hidden Markov models. Application to radiation signals. Ph.D. Thesis, University Paris-Sud Orsay (in French).

Doucet A. 1998. On sequential simulation-based methods for Bayesian filtering. Technical Report, University of Cambridge, Dept. of Engineering, CUED-F-ENG-TR310. Available on the MCMC preprint service at http://www.stats.bris.ac.uk/MCMC/.

Geweke J. 1989. Bayesian inference in Econometrics models using Monte Carlo integration. Econometrica 57: 1317–1339.

Godsill S.J. and Rayner P.J.W. 1998. Digital audio restoration—A statistical model-based approach. Berlin: Springer-Verlag.

Gordon N.J. 1997. A hybrid bootstrap filter for target tracking in clutter. IEEE Transactions on Aerospace and Electronic Systems 33: 353–358.

Gordon N.J., Salmond D.J., and Smith A.F.M. 1993. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE-Proceedings-F 140: 107–113.

Handschin J.E. 1970. Monte Carlo techniques for prediction and filtering of non-linear stochastic processes. Automatica 6: 555–563.

Handschin J.E. and Mayne D.Q. 1969. Monte Carlo techniques to estimate the conditional expectation in multi-stage non-linear filtering. International Journal of Control 9: 547–559.

Higuchi T. 1997. Monte Carlo filtering using the genetic algorithm operators. Journal of Statistical Computation and Simulation 59: 1–23.

Jazwinski A.H. 1970. Stochastic Processes and Filtering Theory. Academic Press.

Kitagawa G. 1987. Non-Gaussian state-space modeling of nonstationary time series. Journal of the American Statistical Association 82: 1032–1063.

Kitagawa G. and Gersch G. 1996. Smoothness Priors Analysis of Time Series. Springer. Lecture Notes in Statistics, Vol. 116.

Kong A., Liu J. S., and Wong W.H. 1994. Sequential imputations and Bayesian missing data problems. Journal of the American Statistical Association 89: 278–288.

Liu J.S. 1996. Metropolized independent sampling with comparison to rejection sampling and importance sampling. Statistics and Computing 6: 113–119.

Liu J.S. and Chen R. 1995. Blind deconvolution via sequential imputation. Journal of the American Statistical Association 90: 567–576.

Liu J.S. and Chen R. 1998. Sequential Monte Carlo methods for dynamic systems. Journal of the American Statistical Association 93: 1032–1044.

MacEachern S.N., Clyde M., and Liu J.S. 1999. Sequential importance sampling for nonparametric Bayes models: The next generation. Canadian Journal of Statistics 27: 251–267.

Müller P. 1991. Monte Carlo integration in general dynamic models. Contemporary Mathematics 115: 145–163.

Müller P. 1992. Posterior integration in dynamic models. Computing Science and Statistics 24: 318–324.

Pitt M.K. and Shephard N. 1999. Filtering via simulation: Auxiliary particle filters. Journal of the American Statistical Association 94: 590–599.

Ripley B.D. 1987. Stochastic Simulation. New York, Wiley.

Rubin D.B. 1988. Using the SIR algorithm to simulate posterior distributions. In: Bernardo J.M., DeGroot M.H., Lindley D.V., and Smith A.F.M. (Eds.), Bayesian Statistics, Vol. 3, Oxford University Press. 395–402.

Smith A.F.M. and Gelfand A.E. 1992. Bayesian statistics without tears: Asampling-resampling perspective. The American Statistician 46: 84–88.

Stewart L. and McCarty P. 1992. The use of Bayesian belief networks to fuse continuous and discrete information for target recognition, tracking and situation assessment. Proceeding Conference SPIE 1699: 177–185.

Svetnik V.B. 1986. Applying the Monte Carlo method for optimum estimation in systems with random disturbances. Automation and Remote Control 47: 818–825.

Tanizaki H. 1993. Nonlinear Filters: Estimation and Applications. Springer. Berlin, Lecture Notes in Economics and Mathematical Systems, Vol. 400.

Tanizaki H. and Mariano R.S. 1994. Prediction, filtering and smoothing in non-linear and non-normal cases using Monte Carlo integration. Journal of Applied Econometrics 9: 163–179.

Tanizaki H. and Mariano R.S. 1998. Nonlinear and non-Gaussian statespace modeling with Monte-Carlo simulations. Journal of Econometrics 83: 263–290.

Tugnait J.K. 1982. Detection and Estimation for abruptly changing systems. Automatica 18: 607–615.

West M. 1993. Mixtures models, Monte Carlo, Bayesian updating and dynamic models. Computer Science and Statistics 24: 325–333.

West M. and Harrison J.F. 1997. Bayesian forecasting and dynamic models, 2nd edn. Springer Verlag Series in Statistics.

Zaritskii V. S., Svetnik V. B., and Shimelevich L.I. 1975. Monte Carlo technique in problems of optimal data processing. Automation and Remote Control 12: 95–103.