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References

  • Albert, J. and S. Chib (1993) Bayes inference via Gibbs sampling of autoregressive time series subject to Markov mean and variance shifts. Journal of Business & Economic Statistics 11(1), 115.
  • Albert, P. S. (1991) A two-state Markov mixture model for a time series of epileptic seizure counts. Biometrics 47(4), 137181.
  • Andrieu, C., A. Doucet and R. Holenstein (2010) Particle Markov chain Monte Carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72(3), 269342.
  • Ashburner, J., K. Friston, A. P. Holmes and J. B. Poline (1999) Statistical Parametric Mapping (SPM2 ed.). Wellcome Department of Cognitive Neurology Available at: http://www.fil.ion.ucl.ac.uk/spm
  • Aston, J. A. D. and D. E. K. Martin (2007) Distributions associated with general runs and patterns in hidden Markov models. The Annals of Applied Statistics 1(2), 585611.
  • Aston, J. A. D., J. Y. Peng and D. E. K. Martin (2011) Implied distributions in multiple change point problems. Statistics and Computing (in press).
  • Baum, L. E., T. Petrie, G. Soules and N. Weiss (1970) A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. The Annals of Mathematical Statistics 41(1), 16471.
  • Cappé, O., E. Moulines and T. Rydén (2005) Inference in Hidden Markov Models. Springer Series in Statistics.
  • Carpenter, J., P. Clifford and P. Fearnhead (1999) An improved particle filter for non-linear problems. IEE Proceedings on Radar Sonar and Navigation 146(1). 27.
  • Chen, J. and A. K. Gupta (2000) Parametric Statistical Change Point Analysis. Birkhauser.
  • Chen, R. and J. Liu (1996) Predictive updating methods with application to Bayesian classification. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 58, 397415.
  • Chib, S. (1998) Estimation and comparison of multiple change-point models. Journal of Econometrics 86, 22141.
  • Chopin, N. (2007) Inference and model choice for sequentially ordered hidden Markov models. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 69(2), 269284.
  • Chopin, N. and F. Pelgrin (2004) Bayesian inference and state number determination for hidden Markov models: an application to the information content of the yield curve about inflation. Journal of Econometrics 123(2), 32744.
  • Davis, R. A., T. C. M. Lee and G. A. Rodriguez-Yam (2006) Structural break estimation for nonstationary time series models. Journal of the American Statistical Association 101, 22339.
  • Del Moral, P., A. Doucet and A. Jasra (2006) Sequential Monte Carlo samplers. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 68(3), 41136.
  • Del Moral, P., A. Doucet and A. Jasra (2011) On adaptive resampling procedures for sequential Monte Carlo methods. Bernoulli. To appear.
  • Douc, R. and O. Cappé (2005) Comparison of resampling schemes for particle filtering, Image and Signal Processing and Analysis, 2005. ISPA 2005. Proceedings of the 4th International Symposium. pp. 6469.
  • Doucet, A. and A. M. Johansen (2011) A tutorial on particle filtering and smoothing: fifteen years later. In The Oxford Handbook of Nonlinear Filtering (eds D. Crisan and B. Rozovskiǐ). Oxford University Press.
  • Durbin, R., S. Eddy, A. Krogh and G. Mitchinson (1998) Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press.
  • Eckley, I., P. Fearnhead and R. Killick (2011) Analysis of changepoint models. In Bayesian Time Series Model (eds D. Barber, A. Cemgil, and S. Chiappa). Cambridge University Press, pp. 21538.
  • Fearnhead, P. (2006) Exact and efficient Bayesian inference for multiple changepoint problems. Statistical Computing 16, 20313.
  • Fearnhead, P. and Z. Liu (2007) On-line inference for multiple changepoint problems. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 69, 589605.
  • Fu, J. C. and M. V. Koutras (1994) Distribution theory of runs: A Markov chain approach. Journal of the American Statistical Association 89(427), 10508.
  • Fu, J. C. and W. Y. W. Lou (2003) Distribution Theory of Runs and Patterns and its Applications: A Finite Markov Chain Imbedding Approach. World Scientific.
  • Gilks, W. R., S. Richardson and D. J. Spiegelhalter (Eds) (1996) Markov Chain Monte Carlo In Practice (first ed.). Chapman & Hall.
  • Gordon N. J., D. J. Salmond and A. F. M. Smith (1993) Novel approach to nonlinear/non-Gaussian Bayesian state estimation. Radar and Signal Processing IEEE Proceedings F 140(2), 10713.
  • Hamilton, J. D. (1989) A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57(2), 35784.
  • Huerta, G. and M. West (1999) Priors and component structures in autoregressive time series models. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 61(4), 88199.
  • Kong, A., J. S. Liu and W. H. Wong (1994) Sequential imputations and Bayesian missing data problems. Journal of the American Statistical Association 89(425), 27888.
  • Lehmann, E. L. and G. Casella (1998) Theory of Point Estimation (Second ed.) Springer.
  • Lindquist, M. (2008) The statistical analysis of fMRI data. Statistical Science 23, 43964.
  • Lindquist, M. A., C. Waugh and T. D. Wager (2007) Modeling state-related fMRI activity using change-point theory. NeuroImage 35(3), 112541.
  • MacDonald, I. L. and W. Zucchini (1997) Monographs on Statistics and Applied Probability 70: Hidden Markov and Other Models for Discrete-valued Time Series. Chapman & Hall/CRC.
  • Neal, R. (2001) Annealed importance sampling. Statistics and Computing 11(2), 12539.
  • Ogawa, S., T. M. Lee, A. R. Kay and D. W. Tank (1990) Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proceeding of the National Academy of Science USA 87(24), 986872.
  • Page, E. S. (1954) Continuous inspection schemes. Biometrika 41, 10015.
  • Peng, J.-Y. (2008) Pattern Statistics in Time Series Analysis. Ph.D. thesis, Department of Computer Science and Information Engineering College of Electrical Engineering and Computer Science, National Taiwan University.
  • Peng, J.-Y., J. A. D. Aston and C.-Y. Liou (2011) Modeling time series and sequences using Markov chain embedded finite automata. International Journal of Innovative Computing Information and Control 7, 40731.
  • Rabiner, L. (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 25786.
  • Roberts, G., A. Gelman and W. Gilks (1997) Weak convergence and optimal scaling of random walk Metropolis algorithms. The Annals of Applied Probability 7(1), 11020.
  • Robinson, L. F., T. D. Wager and M. A. Lindquist (2010) Change point estimation in multi-subject fMRI studies. NeuroImage 49, 158192.
  • Scott, S. (2002) Bayesian methods for hidden Markov models: Recursive computing in the 21st century. Journal of the American Statistical Association 97(457), 33751.
  • Stephens, D. A. (1994) Bayesian retrospective multiple-changepoint identification. Journal of the Royal Statistical Society: Series C (Applied Statistics) 43, 159578.
  • Viterbi, A. (1967) Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. Information Theory, IEEE Transactions 13(2), 2609.
  • Whiteley, N., C. Andrieu and A. Doucet (2009) Particle MCMC for multiple changepoint models. Research report, University of Bristol (09,11).
  • Worsley, K. J., C. Liao, J. A. D. Aston, V. Petre, G. Duncan and A. C. Evans (2002) A general statistical analysis for fMRI data. Neuroimage 15(1), 115.
  • Yao, Y.-C. (1988) Estimating the number of change-points via Schwarz’ criterion. Statistics and Probabilitiy Letters 6, 1819.
  • Yu, S.-Z. (2010) Hidden semi-Markov models. Artificial Intelligence 174(2), 21543.