Multivariate Response and Parsimony for Gaussian Cluster-Weighted Models

Incremental Learning of Multivariate Gaussian Mixture Models

This paper presents a new algorithm for unsupervised incremental learning based on a Bayesian framework. The algorithm, called IGMM (for Incremental Gaussian Mixture Model), creates and continually adjusts a Gaussian Mixture Model consistent to all sequentially presented data. IGMM is particularly useful for on-line incremental clustering of data streams, as encountered in the domain of mobile ...

Multivariate latent Gaussian random field mixture models

a weighted pairwise likelihood approach to multivariate ar(1) models

in this paper, the use of weighted pairwise likelihood instead of the full likelihood in estimating the parameters of the multivariate ar(1) is investigated. a closed formula for typical elements of the godambe information(sandwich information) is presented. some efficiency calculations are also given to discuss the feasibility andcomputational advantages of the weighted pairwise likelihood app...

Detecting community structure: From parsimony to weighted parsimony

Community detection has attracted a great deal of attention in recent years. A parsimony criterion for detecting this structure means that as minimal as possible number of inserted and deleted edges is needed when we make the network considered become a disjoint union of cliques. However, many small groups of nodes are obtained by directly using this criterion to some networks especially for sp...

Covariance Estimation for Multivariate Conditionally Gaussian Dynamic Linear Models

In multivariate time series, the estimation of the covariance matrix of the observation innovations plays an important role in forecasting as it enables the computation of the standardized forecast error vectors as well as it enables the computation of confidence bounds of the forecasts. We develop an on-line, non-iterative Bayesian algorithm for estimation and forecasting. It is empirically fo...