References

1

Abowd, J., Kramarz, F., and Margolis, D. (1999).
High-wage workers and high-wage firms.
Econometrica, 67:251-333.

2

Albert, J. (1996).
Bayesian Computation Using Minitab.
Wadsworth Publishing Company.

3

Andrieu, C., Doucet, A., and Robert, C. (2004).
Computational advances for and from Bayesian analysis.
Statistical Science.
(to appear).

4

Andrieu, C. and Robert, C.P. (2001).
Controlled Markov chain Monte Carlo methods for optimal sampling.
Technical Report 0125, Université Paris Dauphine.

5

Bauwens, L. and Richard, J.F. (1985).
A 1-1 Poly-$t$ random variable generator with application to Monte Carlo integration.
J. Econometrics, 29:19-46.

6

Cappé, O., Guillin, A., Marin, J.M., and Robert, C.P. (2004).
Population Monte Carlo.
J. Comput. Graph. Statist.
(to appear).

7

Cappé, O. and Robert, C.P. (2000).
MCMC: Ten years and still running!
J. American Statist. Assoc., 95(4):1282-1286.

8

Cappé, O. and Rydén, T. (2004).
Hidden Markov Models.
Springer-Verlag.

9

Carlin, B.P. and Chib, S. (1995).
Bayesian model choice through Markov chain Monte Carlo.
J. Roy. Statist. Soc. (Ser. B), 57(3):473-484.

10

Chen, M.H., Shao, Q.M., and Ibrahim, J.G. (2000).
Monte Carlo Methods in Bayesian Computation.
Springer-Verlag.

11

Diebolt, J. and Robert, C.P. (1994).
Estimation of finite mixture distributions by Bayesian sampling.
J. Royal Statist. Soc. Series B, 56:363-375.

12

Doornik, J.A., Hendry, D.F., and Shephard, N. (2002).
Computationally-intensive econometrics using a distributed matrix-programming language.
Philo. Trans. Royal Society London, 360:1245-1266.

13

Doucet, A., de Freitas, N., and Gordon, N. (2001).
Sequential Monte Carlo Methods in Practice.
Springer-Verlag, New York.

14

Gelfand, A.E. and Smith, A.F.M. (1990).
Sampling based approaches to calculating marginal densities.
J. American Statist. Assoc., 85:398-409.

15

Geweke, J. (1999).
Using simulation methods for Bayesian econometric models: Inference, development, and communication (with discussion and rejoinder).
Econometric Reviews, 18:1-126.

16

Gilks, W.R. and Berzuini, C. (2001).
Following a moving target-Monte Carlo inference for dynamic Bayesian models.
J. Royal Statist. Soc. Series B, 63(1):127-146.

17

Gilks, W.R., Roberts, G.O., and Sahu, S.K. (1998).
Adaptive Markov chain Monte Carlo.
J. American Statist. Assoc., 93:1045-1054.

18

Gilks, W.R., Thomas, A., and Spiegelhalter, D.J. (1994).
A language and program for complex Bayesian modelling.
The Statistician, 43:169-178.

19

Gordon, N., Salmond, J., and Smith, A.F.M. (1993).
A novel approach to non-linear/non-Gaussian Bayesian state estimation.
IEEE Proceedings on Radar and Signal Processing, 140:107-113.

20

Green, P.J. (1995).
Reversible jump MCMC computation and Bayesian model determination.
Biometrika, 82(4):711-732.

21

Green, P.J., Hjort, N.L., and Richardson, S. (2003).
Highly Structured Stochastic Systems.
Oxford University Press, Oxford, UK.

22

Haario, H., Saksman, E., and Tamminen, J. (1999).
Adaptive proposal distribution for random walk Metropolis algorithm.
Computational Statistics, 14(3):375-395.

23

Haario, H., Saksman, E., and Tamminen, J. (2001).
An adaptive Metropolis algorithm.
Bernoulli, 7(2):223-242.

24

Hesterberg, T. (1998).
Weighted average importance sampling and defensive mixture distributions.
Technometrics, 37:185-194.

25

Iba, Y. (2000).
Population-based Monte Carlo algorithms.
Trans. Japanese Soc. Artificial Intell., 16(2):279-286.

26

Jeffreys, H. (1961).
Theory of Probability (3rd edition).
Oxford University Press, Oxford, 1939 edition.

27

Liu, J.S. (2001).
Monte Carlo Strategies in Scientific Computing.
Springer-Verlag, New York, NY.

28

McCullagh, P. and Nelder, J. (1989).
Generalized Linear Models.
Chapman and Hall.

29

Meng, X.L. and Wong, W.H. (1996).
Simulating ratios of normalizing constants via a simple identity: a theoretical exploration.
Statist. Sinica, 6:831-860.

30

Metropolis, N. and Ulam, S. (1949).
The Monte Carlo method.
J. American Statist. Assoc., 44:335-341.

31

Neal, R.M. (2003).
Slice sampling (with discussion).
Ann. Statist., 31:705-767.

32

Nobile, A. (1998).
A hybrid Markov chain for the Bayesian analysis of the multinomial probit model.
Statistics and Computing, 8:229-242.

33

Pole, A., West, M., and Harrison, P.J. (1994).
Applied Bayesian Forecasting and Time Series Analysis.
Chapman-Hall, New York.

34

Richardson, S. and Green, P.J. (1997).
On Bayesian analysis of mixtures with an unknown number of components (with discussion).
J. Royal Statist. Soc. Series B, 59:731-792.

35

Robert, C.P. (2001).
The Bayesian Choice.
Springer-Verlag, second edition.

36

Robert, C.P. and Casella, G. (1999).
Monte Carlo Statistical Methods.
Springer-Verlag, New York, NY.

37

Robert, C.P. and Casella, G. (2004).
Monte Carlo Statistical Methods.
Springer-Verlag, New York, second edition.
(to appear).

38

Roeder, K. (1992).
Density estimation with confidence sets exemplified by superclusters and voids in galaxies.
J. American Statist. Assoc., 85:617-624.

39

Shephard, N. and Pitt, M.K. (1997).
Likelihood analysis of non-Gaussian measurement time series.
Biometrika, 84:653-668.

40

Spiegelhalter, D.J., Best, N.G., Carlin, B.P., and van der Linde, A. (2002).
Bayesian measures of model complexity and fit.
J. Royal Statistical Society Series B, 64(3):583-639.

41

Spiegelhalter, D.J., Thomas, A., and Best, N.G. (1999).
WinBUGS Version 1.2 User Manual.
Cambridge.

42

Stavropoulos, P. and Titterington, D.M. (2001).
Improved particle filters and smoothing.
In Doucet, A., deFreitas, N., and Gordon, N., editors, Sequential MCMC in Practice. Springer-Verlag.

43

Tanner, M. and Wong, W. (1987).
The calculation of posterior distributions by data augmentation.
J. American Statist. Assoc., 82:528-550.

44

Von Neumann, J. (1951).
Various techniques used in connection with random digits.
J. Resources of the National Bureau of Standards - Applied Mathematics Series, 12:36-38.