[Propriétés asymptotiques des lois a posteriori sous un modèle incorrect]
Nous étudions les propriétés asymptotiques des lois a posteriori lorsque la distribution des observations est mal spécifiée, c'est-à-dire lorsque la loi a posteriori est construite à partir d'une famille de densités
We investigate the asymptotic properties of posterior distributions when the model is misspecified, i.e. it is contemplated that the observations x1,…,xn might be drawn from a density in a family
Accepté le :
Publié le :
Christophe Abraham 1 ; Benoı̂t Cadre 2
@article{CRMATH_2002__335_5_495_0, author = {Christophe Abraham and Beno{\i}̂t Cadre}, title = {Asymptotic properties of posterior distributions derived from misspecified models}, journal = {Comptes Rendus. Math\'ematique}, pages = {495--498}, publisher = {Elsevier}, volume = {335}, number = {5}, year = {2002}, doi = {10.1016/S1631-073X(02)02520-7}, language = {en}, }
TY - JOUR AU - Christophe Abraham AU - Benoı̂t Cadre TI - Asymptotic properties of posterior distributions derived from misspecified models JO - Comptes Rendus. Mathématique PY - 2002 SP - 495 EP - 498 VL - 335 IS - 5 PB - Elsevier DO - 10.1016/S1631-073X(02)02520-7 LA - en ID - CRMATH_2002__335_5_495_0 ER -
Christophe Abraham; Benoı̂t Cadre. Asymptotic properties of posterior distributions derived from misspecified models. Comptes Rendus. Mathématique, Volume 335 (2002) no. 5, pp. 495-498. doi : 10.1016/S1631-073X(02)02520-7. https://comptes-rendus.academie-sciences.fr/mathematique/articles/10.1016/S1631-073X(02)02520-7/
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- Bayesian Multioutput Feedforward Neural Networks Comparison: A Conjugate Prior Approach, IEEE Transactions on Neural Networks, Volume 17 (2006) no. 1, p. 35 | DOI:10.1109/tnn.2005.860883
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