Bayesian variable selection for finite mixture model of linear regressions
نویسندگان
چکیده
منابع مشابه
Bayesian variable selection for finite mixture model of linear regressions
We propose a Bayesian method for variable selection in the finite mixture model of linear regressions. The model assumes that the observations come from a heterogeneous population which is a mixture of a finite number of sub-populations. Within each sub-population, the response variable can be explained by a linear regression on the predictor variables. So the whole data set can be modeled by a...
متن کاملBayesian Mixture of Probabilistic Linear Regressions for Voice Conversion
The objective of voice conversion is to transform the voice of one speaker to make it sound like another. The GMM-based statistical mapping technique has been proved to be an efficient method for converting voices [1, 2]. In a recent work [3], we generalized this technique to Mixture of Probabilistic Linear Regressions (MPLR) by using general mixture model of source vectors. In this paper, we i...
متن کاملVariational approximations in Bayesian model selection for finite mixture distributions
Variational methods for model comparison have become popular in the neural computing/machine learning literature. In this paper we explore their application to the Bayesian analysis of mixtures of Gaussians. We also consider how the Deviance Information Criterion, or DIC, devised by Spiegelhalter et al. (2002), can be extended to these types of model by exploiting the use of variational approxi...
متن کاملBayesian Variable Selection in Markov Mixture Models
Bayesian methods for variable selection have become increasingly popular in recent years, due to advances in MCMC computational algorithms. Several methods have been proposed in literature in the case of linear and generalized linear models. In this paper we adapt some of the most popular algorithms to a class of non-linear and non-Gaussian time series models, i.e. the Markov mixture models (MM...
متن کاملVariational Bayesian Model Selection for Mixture Distributions
Mixture models, in which a probability distribution is represented as a linear superposition of component distributions, are widely used in statistical modeling and pattern recognition. One of the key tasks in the application of mixture models is the determination of a suitable number of components. Conventional approaches based on cross-validation are computationally expensive, are wasteful of...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computational Statistics & Data Analysis
سال: 2016
ISSN: 0167-9473
DOI: 10.1016/j.csda.2015.09.005