搜索结果: 1-15 共查到“统计逻辑学 Models”相关记录20条 . 查询时间(0.169 秒)
Characterizing A Database of Sequential Behaviors with Latent Dirichlet Hidden Markov Models
LDHMMs sequential data variational inference variational EM behavior modeling sequence classification
2013/6/14
This paper proposes a generative model, the latent Dirichlet hidden Markov models (LDHMM), for characterizing a database of sequential behaviors (sequences). LDHMMs posit that each sequence is generat...
Fast inference in generalized linear models via expected log-likelihoods
Fast inference generalized linear models expected log-likelihoods
2013/6/14
Generalized linear models play an essential role in a wide variety of statistical applications. This paper discusses an approximation of the likelihood in these models that can greatly facilitate comp...
Quantum Annealing for Dirichlet Process Mixture Models with Applications to Network Clustering
Quantum annealing Dirichlet process Stochastic optimization Maximum a posteriori estimation Bayesian nonparametrics
2013/6/17
We developed a new quantum annealing (QA) algorithm for Dirichlet process mixture (DPM) models based on the Chinese restaurant process (CRP). QA is a parallelized extension of simulated annealing (SA)...
MCMC methods for Gaussian process models using fast approximations for the likelihood
MCMC methods for Gaussian process models using fast approximations for the likelihood
2013/6/14
Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $\pi$, fo...
MCMC methods for Gaussian process models using fast approximations for the likelihood
MCMC methods for Gaussian process models using fast approximations for the likelihood
2013/6/14
Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $\pi$, fo...
Model-based dose finding under model uncertainty using general parametric models
Model-based model uncertainty parametric models
2013/6/13
Statistical methodology for the design and analysis of clinical Phase II dose response studies, with related software implementation, are well developed for the case of a normally distributed, homosce...
Ensembling Classification Models Based on Phalanxes of Variables with Applications in Drug Discovery
classification ranking ensemble random forest cluster pha-lanx
2013/4/28
We have proposed an ensemble method which aggregates over clusters of predictor variables. We form the clusters (we call phalanxes) by joining variables together. The variables in a phalanx are good t...
Propagation of initial errors on the parameters for linear and Gaussian state space models
Kalman filter Extended Kalman filter State space mod-els Autoregressive process
2013/4/27
For linear and Gaussian state space models parametrized by $\theta_0 \in \Theta \subset \R^{r}, r \geq 1$ corresponding to the vector of parameters of the model, the Kalman filter gives exactly the so...
Model selection and clustering in stochastic block models with the exact integrated complete data likelihood
Random graphs stochastic block models integrated classication likelihood
2013/4/27
The stochastic block model (SBM) is a mixture model used for the clustering of nodes in networks. It has now been employed for more than a decade to analyze very different types of networks in many sc...
Modeling US house prices by spatial dynamic structural equation models
house prices Bayesian inference dynamic factor models spatio-temporal models cointegration lattice data
2013/4/27
This article proposes a spatial dynamic structural equation model for the analysis of housing prices at the State level in the USA. The study contributes to the existing literature by extending the us...
PReMiuM: An R Package for Profile Regression Mixture Models using Dirichlet Processes
Profile regression Clustering Dirichlet process mixture model
2013/4/27
PReMiuM is a recently developed R package for Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vect...
Concepts and a case study for a flexible class of graphical Markov models
Concepts a case study a flexible class graphical Markov models
2013/4/27
With graphical Markov models, one can investigate complex dependences, summarize some results of statistical analyses with graphs and use these graphs to understand implications of well-fitting models...
Learning AMP Chain Graphs and some Marginal Models Thereof under Faithfulness
Learning AMP Chain Graphs some Marginal Models Thereof under Faithfulness
2013/4/27
This paper deals with chain graphs under the Andersson-Madigan-Perlman (AMP) interpretation. In particular, we present a constraint based algorithm for learning an AMP chain graph a given probability ...
Penalized Likelihood and Bayesian Function Selection in Regression Models
generalized additive model regularization smoothing spike and slab priors
2013/4/27
Challenging research in various fields has driven a wide range of methodological advances in variable selection for regression models with high-dimensional predictors. In comparison, selection of nonl...
Penalized Likelihood and Bayesian Function Selection in Regression Models
generalized additive model regularization smoothing spike and slab priors
2013/4/27
Challenging research in various fields has driven a wide range of methodological advances in variable selection for regression models with high-dimensional predictors. In comparison, selection of nonl...