Recentered importance sampling with applications to Bayesian model validation

Since its introduction in the early 1990s, the idea of using importance sampling (IS) with Markov chain Monte Carlo (MCMC) has found many applications. This article examines problems associated with its application to repeated evaluation of related posterior distributions with a particular focus on Bayesian model validation. We demonstrate that, in certain applications, the curse of dimensionality can be reduced by a simple modification of IS. In addition to providing new theoretical insight into the behavior of the IS approximation in a wide class of models, our result facilitates the implementation of computationally intensive Bayesian model checks. We illustrate the simplicity, computational savings, and potential inferential advantages of the proposed approach through two substantive case studies, notably computation of Bayesian p-values for linear regression models and simulation-based model checking. Supplementary materials including the Appendix and the R code for Section 3.1.2 are available online

MCMC implementation for Bayesian hidden semi-Markov models with illustrative applications

Copyright © Springer 2013. The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-013-9399-zHidden Markov models (HMMs) are flexible, well established models useful in a diverse range of applications. However, one potential limitation of such models lies in their inability to explicitly structure the holding times of each hidden state. Hidden semi-Markov models (HSMMs) are more useful in the latter respect as they incorporate additional temporal structure by explicit modelling of the holding times. However, HSMMs have generally received less attention in the literature, mainly due to their intensive computational requirements. Here a Bayesian implementation of HSMMs is presented. Recursive algorithms are proposed in conjunction with Metropolis-Hastings in such a way as to avoid sampling from the distribution of the hidden state sequence in the MCMC sampler. This provides a computationally tractable estimation framework for HSMMs avoiding the limitations associated with the conventional EM algorithm regarding model flexibility. Performance of the proposed implementation is demonstrated through simulation experiments as well as an illustrative application relating to recurrent failures in a network of underground water pipes where random effects are also included into the HSMM to allow for pipe heterogeneity

Dynamics of the spread of the Bovine Viral Diarrhoea Virus (BVDV) within a dairy herd according to the separation of animals into subgroups

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Dynamics of the spread of the Bovine Viral Diarrhoea Virus (BVDV) within a dairy herd according to the separation of animals into subgroups

International audienc

Influence de la structuration du troupeau en lots sur la propagation du virus de la Diarrhée Virale Bovine (BVDV) en élevage bovin laitier

National audienceThe spread of the Bovine Viral Diarrhoea Virus (BVDV) within a herd is influenced by the herd management. In dairy herds, animals are distributed into subgroups and this induces an heterogeneity in the probability of BVDV transmission. This paper presents a study of the influence of the level of separation between subgroups on the BVDV spread. A simulation model of the BVDV spread was used. In a totally susceptible herd, a freshening non-PI heifer with a PI fetus was introduced. The BVDV spread was simulated assuming different levels of contacts between subgroups (no contact, intermediate or high level of contacts). The influence of this level of contacts between subgroups on the probability of BVDV transmission was considered by defining different BVDV transmission rates. In the results, the BVDV persistence, the extent of infection and the variability of the dynamics of the BVDV spread were found to be influenced by the level of contacts between subgroups. Therefore, the separation of animals into subgroups has to be taken into account to model the BVDV spread in order to assess the control programsLa propagation du virus de la Diarrhée Virale Bovine (BVDV) dans un troupeau est influencée par la structure du troupeau. Dans les troupeaux laitiers en particulier, les animaux sont conduits en lots plus ou moins fortement séparés. Ceci induit une hétérogénéité de la probabilité de transmission du virus et pourrait conduire à accroître la variabilité des dynamiques de propagation. L’objectif de cette étude est d’évaluer l’influence du degré de séparation entre lots sur les dynamiques de propagation du virus BVDV. Un modèle de simulation de la propagation du virus BVDV a été utilisé. Dans un troupeau sans exposition antérieure au virus, une génisse non Infectée Permanente Immunotolérante (IPI) portant un fœtus IPI a été introduite. Différents niveaux de contacts entre lots (absence, intermédiaire, élevé) ont été simulés. L’effet du degré de séparation entre lots sur la transmission horizontale du virus a été pris en compte par des taux de transmission différents. Dans les résultats, la persistance du virus, l’étendue de l’infection et la variabilité des dynamiques de propagation sont influencées par le degré de séparation. La structuration en lots et le degré de séparation doivent donc être pris en compte pour la modélisation de la propagation du virus BVDV en vue de l’élaboration de plans de maîtris

Influence de la structuration du troupeau en lots sur la propagation du virus de la Diarrhée Virale Bovine (BVDV) en élevage bovin laitier

National audienceThe spread of the Bovine Viral Diarrhoea Virus (BVDV) within a herd is influenced by the herd management. In dairy herds, animals are distributed into subgroups and this induces an heterogeneity in the probability of BVDV transmission. This paper presents a study of the influence of the level of separation between subgroups on the BVDV spread. A simulation model of the BVDV spread was used. In a totally susceptible herd, a freshening non-PI heifer with a PI fetus was introduced. The BVDV spread was simulated assuming different levels of contacts between subgroups (no contact, intermediate or high level of contacts). The influence of this level of contacts between subgroups on the probability of BVDV transmission was considered by defining different BVDV transmission rates. In the results, the BVDV persistence, the extent of infection and the variability of the dynamics of the BVDV spread were found to be influenced by the level of contacts between subgroups. Therefore, the separation of animals into subgroups has to be taken into account to model the BVDV spread in order to assess the control programsLa propagation du virus de la Diarrhée Virale Bovine (BVDV) dans un troupeau est influencée par la structure du troupeau. Dans les troupeaux laitiers en particulier, les animaux sont conduits en lots plus ou moins fortement séparés. Ceci induit une hétérogénéité de la probabilité de transmission du virus et pourrait conduire à accroître la variabilité des dynamiques de propagation. L’objectif de cette étude est d’évaluer l’influence du degré de séparation entre lots sur les dynamiques de propagation du virus BVDV. Un modèle de simulation de la propagation du virus BVDV a été utilisé. Dans un troupeau sans exposition antérieure au virus, une génisse non Infectée Permanente Immunotolérante (IPI) portant un fœtus IPI a été introduite. Différents niveaux de contacts entre lots (absence, intermédiaire, élevé) ont été simulés. L’effet du degré de séparation entre lots sur la transmission horizontale du virus a été pris en compte par des taux de transmission différents. Dans les résultats, la persistance du virus, l’étendue de l’infection et la variabilité des dynamiques de propagation sont influencées par le degré de séparation. La structuration en lots et le degré de séparation doivent donc être pris en compte pour la modélisation de la propagation du virus BVDV en vue de l’élaboration de plans de maîtris

16 - Cyberknife M6: Peripheral dose evaluation in brain treatments

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25 - Practical implementation of a patient specific QA on Cyberknife using Gafchromic® EBT3 film: One-year results analysis

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