Relabelling in Bayesian mixture models by pivotal units
نویسندگان
چکیده
منابع مشابه
Probabilistic relabelling strategies for the label switching problem in Bayesian mixture models
The label switching problem is caused by the likelihood of a Bayesian mixture model being invariant to permutations of the labels. The permutation can change multiple times between Markov Chain Monte Carlo (MCMC) iterations making it difficult to infer component-specific parameters of the model. Various so-called ‘relabelling’ strategies exist with the goal to ‘undo’ the label switches that hav...
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 کاملConstructing Bayesian Nite Mixture Models by the Em Algorithm
In this paper we explore the use of nite mixture models for building decision support systems capable of sound probabilistic inference. Finite mixture models have many appealing properties: they are computationally eecient in the prediction (reasoning) phase, they are universal in the sense that they can approximate any problem domain distribution, and they can handle multimod-ality well. We pr...
متن کاملFlexible and Robust Bayesian Classification by Finite Mixture Models
The regularized Mahalanobis distance is proposed in the framework of finite mixture models to avoid commonly faced numerical difficulties encountered with EM. Its principle is applied to Gaussian and Student-t mixtures, resulting in reliable density estimates, the model complexity being kept low. Besides, the regularized models are robust to various noise types. Finally, it is shown that the qu...
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: Statistics and Computing
سال: 2017
ISSN: 0960-3174,1573-1375
DOI: 10.1007/s11222-017-9774-2