Surgical workflow analysis with Gaussian mixture multivariate autoregressive (GMMAR) models: a simulation study

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

Prediction with Mixture Autoregressive Models

Mixture autoregressive (MAR) models have the attractive property that the shape of the conditional distribution of a forecast depends on the recent history of the process. In particular, it may have a varying number of modes over time. We show that the distributions of the multi-step predictors in MAR models are also mixtures and specify them analytically. In the important case when the origina...

Multivariate Autoregressive Mixture Models for Music Auto-Tagging

We propose the multivariate autoregressive model for content based music auto-tagging. At the song level our approach leverages the multivariate autoregressive mixture (ARM) model, a generative time-series model for audio, which assumes each feature vector in an audio fragment is a linear function of previous feature vectors. To tackle tagmodel estimation, we propose an efficient hierarchical E...

a study on thermodynamic models for simulation of 1,3 butadiene purification columns

attempts have been made to study the thermodynamic behavior of 1,3 butadiene purification columns with the aim of retrofitting those columns to more energy efficient separation schemes. 1,3 butadiene is purified in two columns in series through being separated from methyl acetylene and 1,2 butadiene in the first and second column respectively. comparisons have been made among different therm...