A new Markov chain Monte Carlo method for the Bayesian analysis of finite mixture distributions with an unknown number of components is presented. The sampler is characterized by a state space consisting only of the number of components and the latent allocation variables. Its main advantage is that it can be used, with minimal changes, for mixtures of components from any parametric family, under the assumption that the component parameters can be integrated out of the model analytically. Artificial and real data sets are used to illustrate the method and mixtures of univariate and of multivariate normals are explicitly considered. The problem of label switching, when parameter inference is of interest, is addressed in a post-processing stage.

This document supplements a paper Komárek (2009) and shows an analysis of the Old Faithful Geyser data introduced in Härdle (1991) using the R package mixAK. The data have been analysed using mixtures by several researchers, e.g., Stephens (2000), Dellaportas and Papageorgiou (2006). 1

This document supplements a paper Komárek (2009) and shows an analysis of the Old Faithful Geyser data introduced in Härdle (1991) using the R package mixAK. The data have been analysed using mixtures by several researchers, e.g., Stephens (2000), Dellaportas and Papageorgiou (2006). 1

This document supplements a paper Komárek (2009) and shows an analysis of the Old Faithful Geyser data introduced in Härdle (1991) using the R package mixAK. The data have been analysed using mixtures by several researchers, e.g., Stephens (2000), Dellaportas and Papageorgiou (2006). 1

This paper presents a new graph cut-based multiple active contour algorithm to detect optimal boundaries and regions in images without initial contours. The task of multiple active contours is framed as a partitioning problem by assuming that image data are generated from a finite mixture model with unknown number of components. Then, the partitioning problem is solved within a divisive graph cut framework where multi-way minimum cuts for multiple contours are efficiently computed in a top-down way through a swap move of binary labels. A split move is integrated into that framework to estimate the model parameters associated with regions without the use of initial contours and seed points. The number of regions is also estimated as a part of the algorithm. Experimental results of boundary and region detection of natural images are presented to demonstrate the effectiveness of the proposed algorithm. 1.

This document supplements a paper Komárek (2009) and shows an analysis of the Old Faithful Geyser data introduced in Härdle (1991) using the R package mixAK. The data have been analysed using mixtures by several researchers, e.g., Stephens (2000), Dellaportas and Papageorgiou (2006). 1

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