Understanding predictive information criteria for Bayesian models
نویسندگان
چکیده
منابع مشابه
Understanding predictive information criteria for Bayesian models
We review the Akaike, deviance, and Watanabe-Akaike information criteria from a Bayesian perspective, where the goal is to estimate expected out-of-sample-prediction error using a biascorrected adjustment of within-sample error. We focus on the choices involved in setting up these measures, and we compare them in three simple examples, one theoretical and two applied. The contribution of this p...
متن کاملExtended Bayesian Information Criteria for Gaussian Graphical Models
Gaussian graphical models with sparsity in the inverse covariance matrix are of significant interest in many modern applications. For the problem of recovering the graphical structure, information criteria provide useful optimization objectives for algorithms searching through sets of graphs or for selection of tuning parameters of other methods such as the graphical lasso, which is a likelihoo...
متن کاملBayesian Posterior Predictive Checks for Complex Models
In sociological research, it is often difficult to compare nonnested models and to evaluate the fit of models in which outcome variables are not normally distributed. In this article, the authors demonstrate the utility of Bayesian posterior predictive distributions specifically, as well as a Bayesian approach to modeling more generally, in tackling these issues. First, they review the Bayesian...
متن کاملHierarchical Bayesian models for unsupervised scene understanding
For very large datasets with more than a few classes, producing ground-truth data can represent a substantial, and potentially expensive, human effort. This is particularly evident when the datasets have been collected for a particular purpose, e.g. scientific inquiry, or by autonomous agents in novel and inaccessible environments. In these situations there is scope for the use of unsupervised ...
متن کاملNonparametric Bayesian Models for Spoken Language Understanding
In this paper, we propose a new generative approach for semantic slot filling task in spoken language understanding using a nonparametric Bayesian formalism. Slot filling is typically formulated as a sequential labeling problem, which does not directly deal with the posterior distribution of possible slot values. We present a nonparametric Bayesian model involving the generation of arbitrary na...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Statistics and Computing
سال: 2013
ISSN: 0960-3174,1573-1375
DOI: 10.1007/s11222-013-9416-2