Asymptotic behaviour of a BIPF algorithm with an improper target
Kybernetika, Tome 45 (2009) no. 2, pp. 169-188 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

Voir la notice de l'article

The BIPF algorithm is a Markovian algorithm with the purpose of simulating certain probability distributions supported by contingency tables belonging to hierarchical log-linear models. The updating steps of the algorithm depend only on the required expected marginal tables over the maximal terms of the hierarchical model. Usually these tables are marginals of a positive joint table, in which case it is well known that the algorithm is a blocking Gibbs Sampler. But the algorithm makes sense even when these marginals do not come from a joint table. In this case the target distribution of the algorithm is necessarily improper. In this paper we investigate the simplest non trivial case, i. e. the $2\times2\times2$ hierarchical interaction. Our result is that the algorithm is asymptotically attracted by a limit cycle in law.
The BIPF algorithm is a Markovian algorithm with the purpose of simulating certain probability distributions supported by contingency tables belonging to hierarchical log-linear models. The updating steps of the algorithm depend only on the required expected marginal tables over the maximal terms of the hierarchical model. Usually these tables are marginals of a positive joint table, in which case it is well known that the algorithm is a blocking Gibbs Sampler. But the algorithm makes sense even when these marginals do not come from a joint table. In this case the target distribution of the algorithm is necessarily improper. In this paper we investigate the simplest non trivial case, i. e. the $2\times2\times2$ hierarchical interaction. Our result is that the algorithm is asymptotically attracted by a limit cycle in law.
Classification : 60J05, 60J22, 62F15, 62H17, 65C40
Keywords: log-linear models; marginal problem; null Markov chains
@article{KYB_2009_45_2_a0,
     author = {Asci, Claudio and Piccioni, Mauro},
     title = {Asymptotic behaviour of a {BIPF} algorithm with an improper target},
     journal = {Kybernetika},
     pages = {169--188},
     year = {2009},
     volume = {45},
     number = {2},
     mrnumber = {2518147},
     zbl = {1170.60326},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/KYB_2009_45_2_a0/}
}
TY  - JOUR
AU  - Asci, Claudio
AU  - Piccioni, Mauro
TI  - Asymptotic behaviour of a BIPF algorithm with an improper target
JO  - Kybernetika
PY  - 2009
SP  - 169
EP  - 188
VL  - 45
IS  - 2
UR  - http://geodesic.mathdoc.fr/item/KYB_2009_45_2_a0/
LA  - en
ID  - KYB_2009_45_2_a0
ER  - 
%0 Journal Article
%A Asci, Claudio
%A Piccioni, Mauro
%T Asymptotic behaviour of a BIPF algorithm with an improper target
%J Kybernetika
%D 2009
%P 169-188
%V 45
%N 2
%U http://geodesic.mathdoc.fr/item/KYB_2009_45_2_a0/
%G en
%F KYB_2009_45_2_a0
Asci, Claudio; Piccioni, Mauro. Asymptotic behaviour of a BIPF algorithm with an improper target. Kybernetika, Tome 45 (2009) no. 2, pp. 169-188. http://geodesic.mathdoc.fr/item/KYB_2009_45_2_a0/

[1] D. Z. Albert: Quantum Mechanics and Experience. Harvard College, Cambridge 1992. | MR

[2] C. Asci, G. Letac, and M. Piccioni: Beta-hypergeometric distributions and random continued fractions. Statist. Probab. Lett. 78 (2008), 1711–1721. | MR

[3] C. Asci and M. Piccioni: The IPF algorithm when the marginal problem is unsolvable: the simplest case. Kybernetika 39 (2003), 731–737. | MR

[4] C. Asci and M. Piccioni: Functionally compatible local characteristics for the local specification of priors in graphical models. Scand. J. Statist. 34 (2007), 829–840. | MR

[5] I. Csiszár: I-divergence geometry of probability distributions and minimization problems. Ann. Probab. 3 (1975), 146–158. | MR

[6] A. P. Dawid and S. L. Lauritzen: Hyper-Markov laws in the statistical analysis of decomposable graphical models. Ann. Statist. 21 (1993), 1272–1317. | MR

[7] D. A. van Dyk and X. Meng: The art of data augmentation. With discussions, and a rejoinder by the authors. J. Comput. Graph. Statist. 10 (2001), 1–111. | MR

[8] A. Einstein, B. Podolsky, and N. Rosen: Can the quantum-mechanical description of physical reality be considered complete? Phys. Rev. 47 (1935), 777–780.

[9] A. Gelman, J. B. Carlin, D. B. Rubin, and H. S. Stern: Bayesian Data Analysis. Chapman and Hall, London 1995. | MR

[10] J. P. Hobert and G. Casella: Functional compatibility, Markov chains, and Gibbs sampling with improper posteriors. J. Comput. Graph. Statist. 7 (1998), 42–60. | MR

[11] S. L. Lauritzen: Graphical Models. Clarendon Press, Oxford 1996. | MR | Zbl

[12] S. L. Lauritzen and T. S. Richardson: Chain graph models and their causal interpretations (with discussion). J. Roy. Statist. Soc. Ser. B 64 (2002), 321–348. | MR

[13] S. P. Meyn and R. L. Tweedie: Markov Chains and Stochastic Stability. Springer-Verlag, London 1993. | MR

[14] M. Piccioni: Independence structure of natural conjugate densities to exponential families and the Gibbs’ sampler. Scand. J. Statist. 27 (2000), 111–127. | MR | Zbl

[15] E. D. Rainville: Special Functions. MacMillan, New York 1960. | MR | Zbl

[16] J. L. Schafer: Analysis of Incomplete Multivariate Data. Chapman and Hall, London 1997. | MR | Zbl

[17] R. L. Tweedie: R-theory for Markov chains on a general state space I, II. Ann. Probab. 2 (1974), 840–878. | MR

[18] D. Williams: Weighing the Odds. Cambridge University Press, Cambridge 2001. | MR | Zbl