Mixtures of Latent Variable Models for Density Estimation and Classification
نویسندگان
چکیده
منابع مشابه
Using multivariate generalized linear latent variable models to measure the difference in event count for stranded marine animals
BACKGROUND AND OBJECTIVES: The classification of marine animals as protected species makes data and information on them to be very important. Therefore, this led to the need to retrieve and understand the data on the event counts for stranded marine animals based on location emergence, number of individuals, behavior, and threats to their presence. Whales are g...
متن کاملSingle Factor Transformation Priors for Density Regression
Although discrete mixture modeling has formed the backbone of the literature on Bayesian density estimation incorporating covariates, the use of discrete mixtures leads to some well known disadvantages. Avoiding discrete mixtures, we propose a flexible class of priors based on random nonlinear functions of a uniform latent variable with an additive residual. These priors are related to Gaussian...
متن کاملTitle of the ESTIMATION AND MODEL SELECTION FOR Dissertation FINITE MIXTURES OF LATENT INTERACTION MODELS
Title of the ESTIMATION AND MODEL SELECTION FOR Dissertation FINITE MIXTURES OF LATENT INTERACTION MODELS Jui-Chen Hsu, Doctor of Philosophy, 2011 Directed by Professor Gregory R. Hancock, Department of Measurement, Statistics and Evaluation Professor Jeffrey R. Harring, Department of Measurement, Statistics and Evaluation Latent interaction models and mixture models have received considerable ...
متن کاملNonparametric Estimation of Multi-View Latent Variable Models
Spectral methods have greatly advanced the estimation of latent variable models, generating a sequence of novel and efficient algorithms with strong theoretical guarantees. However, current spectral algorithms are largely restricted to mixtures of discrete or Gaussian distributions. In this paper, we propose a kernel method for learning multi-view latent variable models, allowing each mixture c...
متن کاملModel-based Density Estimation by Independent Factor Analysis
In this paper we propose a model based density estimation method which is rooted in Independent Factor Analysis (IFA). IFA is in fact a generative latent variable model, whose structure closely resembles the one of an ordinary factor model but which assumes that the latent variables are mutually independent and distributed according to Gaussian mixtures. From these assumptions, the possibility ...
متن کامل