Bayesian Variable Selection in Markov Mixture Models
نویسندگان
چکیده
منابع مشابه
Bayesian 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 کاملBayesian variable order Markov models
We present a simple, effective generalisation of variable order Markov models to full online Bayesian estimation. The mechanism used is close to that employed in context tree weighting. The main contribution is the addition of a prior, conditioned on context, on the Markov order. The resulting construction uses a simple recursion and can be updated efficiently. This allows the model to make pre...
متن کاملBayesian Portfolio Selection in a Markov Switching Gaussian Mixture Model
Departure from normality poses implementation barriers to the Markowitz mean-variance portfolio selection. When assets are affected by common and idiosyncratic shocks, the distribution of asset returns may exhibit Markov switching regimes and have a Gaussian mixture distribution conditional on each regime. The model is estimated in a Bayesian framework using the Gibbs sampler. An application to...
متن کاملBayesian variable selection for latent class models.
In this article, we develop a latent class model with class probabilities that depend on subject-specific covariates. One of our major goals is to identify important predictors of latent classes. We consider methodology that allows estimation of latent classes while allowing for variable selection uncertainty. We propose a Bayesian variable selection approach and implement a stochastic search G...
متن کاملDiscriminative Variable Subsets in Bayesian Classification with Mixture Models
We discuss the evaluation of subsets of variables for the discriminative evidence they provide in multivariate mixture modeling for classification. Novel development of Bayesian classification analysis uses a natural measure of concordance between mixture component densities, and defines an effective and computationally feasible method for assessing and prioritizing subsets of variables accordi...
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: Communications in Statistics - Simulation and Computation
سال: 2007
ISSN: 0361-0918,1532-4141
DOI: 10.1080/03610910701459956