Clustering in Genralized Linear Mixed Model using Dirichlet 在使用Dirichlet广义线性混合模型聚类.pptVIP

Clustering in Generalized Linear Mixed Model Using Dirichlet Process Mixtures Ya Xue Xuejun Liao April 1, 2005 Introduction Concept drift is in the framework of generalized linear mixed model, but brings new question of exploiting the structuring of auxiliary data. Mixtures with a countably infinite number of components can be handled in a Bayesian framework by employing Dirichlet process priors. Outline Part I: generalized linear mixed model Generalized linear model (GLM) Generalized linear mixed model (GLMM) Advanced applications Bayesian feature selection in GLMM Part II: nonparametric method Chinese restaurant process Dirichlet process (DP) Dirichlet process mixture models Variational inference for Dirichlet process mixtures Part I Generalized Linear Mixed Model Generalized Linear Model (GLM) A linear model specifies the relationship between a dependent (or response) variable Y, and a set of predictor variables, Xs, so that GLM is a generalization of normal linear regression models to exponential family (normal, Poisson, Gamma, binomial, etc). Generalized Linear Model (GLM) GLM differs from linear model in two major respects: The distribution of Y can be non-normal, and does not have to be continuous. Y still can be predicted from a linear combination of Xs, but they are connected via a link function. Generalized Linear Model(GLM) DDE Example: binomial distribution Scientific interest: does DDE exposure increase the risk of cancer? Test on rats. Let i index rat. Dependent variables: Independent variable: dose of DDE exposure, denoted by xi. Generalized Linear Model(GLM) Likelihood function of yi: Choosing the canonical link , the likelihood function becomes GLMM – Basic Model Returning to the DDE example, 19 labs all over the world participated this bioassay. There are unmeasured factors that vary between the different labs. For example, rodent diet. GLMM is an extension of t