[PDF] Top 20 Dirichlet Process Mixtures of Generalized Linear Models
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Dirichlet Process Mixtures of Generalized Linear Models
... tree models, such as classification and regression trees (CART) (Brieman et ...include linear models (Chipman et ...The Dirichlet process has been applied to regression ...Gaussian ... See full document
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Improving Prediction from Dirichlet Process Mixtures via Enrichment
... conditional models can approximate a wide range of response distributions that may change flexibly with the ...conditional models based on dependent ... See full document
31
Modeling and visualizing uncertainty in gene expression clusters using Dirichlet process mixtures
... Zoubin Ghahramani received the BA and BSE degrees from the University of Pennsylvania and the PhD degree from MIT, in 1995, working with Professor Mike Jordan. He is a professor of information engineering at the ... See full document
15
Improving prediction from Dirichlet process mixtures via enrichment
... Dirichlet process (DP) mixture models have become popular tools for Bayesian nonparamet- ric ...the Dirichlet process and leads to improved ... See full document
32
Maximum Likelihood for Gaussian Process Classification and Generalized Linear Mixed Models under Case-Control Sampling
... through generalized linear mixed models (GLMMs; McCulloch et ...Such models associate sampled units with latent variables, and express the dependencies through covariance matrices of latent ... See full document
30
Fitting Finite Mixtures of Generalized Linear Regressions on Motor Insurance Claims
... The E- and M-steps are repeated until the likelihood improvement falls under a pre-specified threshold or a maximum number of iterations is reached. The expectation- maximization (EM) algorithm cannot be used for mixture ... See full document
5
Posterior Predictive Checks for the Generalized Pareto Distribution Based on a Dirichlet Process Prior
... statistical models has been gaining currency as one of several existing techniques for Bayesian evaluation of model goodness of ...theory models and finds that the PPC procedure hit rates appear to be ... See full document
9
Generalized Linear Models in Vehicle Insurance
... The generalized linear models (GLMs) are used for the estimation of AFC in this ...used linear regression and the advantages of GLMs are ...We process a data set based on 57 410 one- ... See full document
6
Generalized linear mixed models for count data
... traditional linear statistical models (LMs) have been developed primarily for normally distributed ...The generalized linear models (GLMs [28]) extend LMs to include a broader class of ... See full document
97
Sensor based human activity mining using Dirichlet process mixtures of directional statistical models
... the sampler is set as 1; i.e. all the data are from one cluster. Each chain runs 200 iterations. The reported Ks are the mean of the 10 modes of the ten chains and NMIs are the average of the ten runs with the first half ... See full document
10
Nonlinear Models Using Dirichlet Process Mixtures
... simple linear models has been explored extensively in the field of machine ...for models that are comprised of several local models (experts) each handling a subset of ...such models, ... See full document
22
Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics
... The natural extension to examining the diversity in an individual sample is to look at patterns across samples from similar environments. Barcoding allows multiple samples to be sequenced in a single run but difficulties ... See full document
15
A Signal-to-Noise Ratio Estimator for Generalized Linear Model Systems
... point process is a time-series of 0-1 random events that occur in continuous ...point process models that relate spiking activity to putative stimuli and biophysical ctors with the model parameters ... See full document
7
Dirichlet multinomial mixtures : generative models for microbial metagenomics
... The natural extension to examining the diversity in an individual sample is to look at patterns across samples from similar environments. Barcoding allows multiple samples to be sequenced in a single run but difficulties ... See full document
16
Ridge regression and diagnostics in generalized linear models
... In linear regression, the added variable plot and the partial residual plot are two well known diagnostic displays that have been suggested for studying the influence of an observation on a single regression ... See full document
133
Assessing Puberty in Ex Situ Cheetahs (Acinonyx jubatus) via Growth Patterns and Fecal Hormone Metabolites.
... Despite the ever growing library of information on adult cheetahs, longitudinal hormone data on young cheetahs, including timing of puberty, are scarce. Puberty is the process of achieving the physiological ... See full document
143
Unsupervised and Constrained Dirichlet Process Mixture Models for Verb Clustering
... The performance of the DPMM is 1%-3% lower than that of Sun et al. As expected, the differ- ence in V-measure is smaller since the DPMM discovers a larger number of clusters, while for VI it is larger. The slightly ... See full document
9
On the goodness of fit of generalized linear geostatistical models
... We have introduced a generalization of Zhang’s coefficient of determination to quantify the proportion of explained variation in the outcome of interest by the covariates and/or the residual spatial random effects. This ... See full document
7
Regression via Clustering using Dirichlet Mixtures
... We will compare our method with several nonparametric methods. A lot of meth- ods using local smoothing such as the kernel method and spline smoothing in the univariate case were provided by many authors. These methods ... See full document
99
Statistical Analysis of Linear and Non Linear Smoothing Techniques under the Autoregressive (AR) and Generalized Auotregressive Conditional Heteroscedastic (Garch) Models
... Non-linear smoothers based on the extreme selectors have been developed as a class of smoothing method with very powerful properties and ideally suitable for application to data having impulsive noise, the type of ... See full document
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