- Referenced in 422 articles
- learning the structure in an undirected Gaussian graphical model, using ℓ 1 regularization to control...
- Referenced in 129 articles
- families of models, including log-linear models, Gaussian models, and models for mixed discrete...
- Referenced in 339 articles
- with Bayesian hierarchical models. Unfortunately, fitting such models involves computationally intensive Markov chain Monte Carlo ... encompassing a wide variety of Gaussian spatial process models for univariate as well as multivariate...
- Referenced in 505 articles
- model. Two recent additions are the multiresponse gaussian, and the grouped multinomial. The algorithm uses...
- Referenced in 69 articles
- approximation to a class of near-Gaussian latent models. This work extends the integrated nested ... models outside the scope of latent Gaussian models, where independent components of the latent field ... flexibility and challenge the Gaussian assumptions of some of the model components in a straightforward...
- Referenced in 122 articles
- randomized drift) inverse Gaussian distribution to survival data. The model is described in Aalen ... been randomized with a Gaussian distribution. The model allows covariates to influence starting values...
- Referenced in 70 articles
- package pgmm: Parsimonious Gaussian mixture models. Carries out model-based clustering or classification using parsimonious ... Gaussian mixture models...
- Referenced in 65 articles
- models such as ARMA and cubic spline models in state space form. Basic functions ... used for implementing, fitting and analysing Gaussian models relevant to many areas of econometrics...
- Referenced in 128 articles
- Linear and Nonlinear Mixed Effects Models , Fit and compare Gaussian linear and nonlinear mixed-effects...
- Referenced in 87 articles
- such as CART and random forest; Gaussian process models (Kriging), and combinations of di erent...
- Referenced in 77 articles
- algorithm for for exploring spaces of Gaussian Graphical Models...
- Referenced in 295 articles
- statistics and image analysis are familiar with Gaussian Markov Random Fields (GMRFs), and they ... longitudinal and survival data, spatio-temporal models, graphical models, and semi-parametric statistics. With ... remains no comprehensive reference on the subject.par Gaussian Markov Random Fields: Theory and Applications provides ... GMRFs in complex hierarchical models, in which statistical inference is only possible using Markov Chain...
- Referenced in 39 articles
- easier to modify; (2) besides fitting Gaussian graphical models, it also provides functions for fitting ... high dimensional semiparametric Gaussian copula models; (3) more functions like data-dependent model selection, data...
- Referenced in 36 articles
- Mixture Modeling (MIXMOD) program fits mixture models to a given data set for the purposes ... multivariate Gaussian mixtures, and fourteen different Gaussian models can be distinguished according to different assumptions...
- Referenced in 21 articles
- Inference in Graphical Gaussian Models with Edge and Vertex Symmetries , Estimation, model selection and other ... aspects of statistical inference in Graphical Gaussian models with edge and vertex symmetries (Graphical Gaussian...
- Referenced in 39 articles
- package tgp: Bayesian treed Gaussian process models. Bayesian nonstationary, semiparametric nonlinear regression and design ... treed Gaussian processes (GPs) with jumps to the limiting linear model (LLM). Special cases also...
- Referenced in 23 articles
- copula factor models for mixed data. Gaussian factor models have proven widely useful for parsimoniously ... variables, using latent Gaussian variables or through generalized latent trait models accommodating measurements ... novel class of Bayesian Gaussian copula factor models that decouple the latent factors from...
- Referenced in 58 articles
- latin hypercube and updates a Gaussian processes surrogate model of the search landscape after every...
- Referenced in 19 articles
- package GPfit: Gaussian Processes Modeling. A computationally stable approach of fitting a Gaussian Process ... model to a deterministic simulator. Gaussian process (GP) models are commonly used statistical metamodels...
- Referenced in 33 articles
- members, e.g. metamodeling (polynomial chaos expansions, Gaussian process modelling, a.k.a. Kriging, low-rank tensor approximations...