- Referenced in 170 articles
- applied to calculate posterior model probabilities for a variety of time series Bayesian models, where...
- Referenced in 69 articles
- robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished ... space, such as the space of all probability distributions or the space of all regression ... implementation of some Bayesian nonparametric and semiparametric models in R, DPpackage. Currently, DPpackage includes models...
- Referenced in 147 articles
- length probabilities and waiting-time probabilities for basic queueing models (M/G/1 queue, M/M/c queue, M/D/c...
- Referenced in 41 articles
- BLOG: probabilistic models with unknown objects. This paper introduces and illustrates ... BLOG, a formal language for defining probability models over worlds with unknown objects and identity ... certain acyclicity constraints, every BLOG model specifies a unique probability distribution over first-order model...
- Referenced in 57 articles
- Generative Topographic Mapping. Latent variable models represent the probability density of data in a space ... form of non-linear latent variable model called the Generative Topographic Mapping for which...
- Referenced in 29 articles
- present a detailed asymptotic analysis of model consistency of the Lasso. For various decays ... compute asymptotic equivalents of the probability of correct model selection (i.e., variable selection ... variables that should enter the model with probability tending to one exponentially fast, while ... selects all other variables with strictly positive probability. We show that this property implies that...
- Referenced in 185 articles
- conditional autoregressive value at risk (CAViaR) model specifies the evolution of the quantile over time ... Utilizing the criterion that each period the probability of exceeding the VaR must be independent ... information, we introduce a new test of model adequacy, the dynamic quantile test. Applications...
- Referenced in 51 articles
- several ABC algorithms for performing parameter estimation, model selection, and goodness-of-fit. Cross-validation ... calculate the misclassification probabilities of different models...
- Referenced in 34 articles
- quickly identifies regions of high posterior probability over models. We describe algorithmic and modeling aspects...
- Referenced in 72 articles
- directed graphical models. BNT supports many kinds of nodes (probability distributions), exact and approximate inference...
- Referenced in 20 articles
- elements. We present a probabilistic XML model that addresses all of these challenges. We devise ... query operations using our probability model, and demonstrate the efficiency of our implementation experimentally...
- Referenced in 317 articles
- statistical methods and material on statistical modelling in R. The book is written ... prerequisite is an honest course in probability and statistics. Finally, let us note that...
- Referenced in 27 articles
- best understood paradigm for modeling and manipulating uncertain information. Probabilities of complex events ... viewpoint of probability theory. (1) We propose a probabilistic relational data model and a genericprobabilistic...
- Referenced in 36 articles
- suitable for various conference models. It is currently probably the most commonly used conference management...
- Referenced in 53 articles
- model are combined with the selections made during a search to estimate the probability associated ... These probabilities are then used to select images for display. Details of our model...
- Referenced in 33 articles
- models is an extension of traditional linear models that allows the mean of a population ... nonlinear link function and allows the response probability distribution to be any member ... family of distributions. Many widely used statistical models are generalized linear models. These include classical ... linear models by the selection of an appropriate link function and response probability distribution...
- Referenced in 8 articles
- package pmr: Probability Models for Ranking Data. Descriptive statistics (mean rank, pairwise frequencies, and marginal ... Saaty’s and Koczkodaj’s inconsistencies), probability models (Luce models, distance-based models, and rank...
- Referenced in 10 articles
- Bayes factors from BIEMS. Furthermore, posterior model probabilities of constrained models are provided which allows...
- Referenced in 11 articles
- types of models. With a closed population model, the goal of the analysis ... which the capture probabilities ... animals vary. Rcapture features several models for these capture probabilities that lead to different estimators ... provision of several new options for modeling capture probabilities heterogeneity between animals in both closed...
- Referenced in 18 articles
- localizers to perform detection. They apply the model to an image at multiple locations ... into regions and predicts bounding boxes and probabilities for each region. These bounding ... boxes are weighted by the predicted probabilities. Our model has several advantages over classifier-based...