Change detection is an important part of image interpretation and automated geographical data collection. In this paper we show a reduced rank regression mixture model for the verification of image changes detected by a human operator. Maximum likelihood estimators are used to learn the operator behaviour. Then, the operator uses the trained system to validate the image changes found. Computati...

Sometimes a larger dataset needs to be reduced to just a few points, and it is desirable that these points be representative of the whole dataset. If the future uses of these points are not fully specified in advance, standard decision-theoretic approaches will not work. We present here methodology for choosing a small representative sample based on a mixture modeling approach.

This paper describes a Bayesian technique for distinguishing melody notes from grace notes in a recorded performance. A mixture model is used to describe probabilities of note durations, and a graphical model relates contextual information (the pitches of three successive notes). Practical examples based on MIDIencoded bagpipe performances are given.

In this research, a latent class vector model for pairwise data is formulated. As compared to the basic vector model, this model yields consistent estimates of the parameters since the number of parameters to be estimated does not increase with the number of subjects. The result of the analysis reveals that the model was stable and could classify each subject to the latent classes representing ...

The problem of modelling football data has become increasingly popular in the last few years and many different models have been proposed with the aim of estimating the characteristics that bring a team to lose or win a game, or to predict the score of a particular match. We propose a Bayesian hierarchical model to address both these aims and test its predictive strength on data about the Itali...

We have developed a sophisticated statistical model for predicting the hitting performance of Major League baseball players. The Bayesian paradigm provides a principled method for balancing past performance with crucial covariates, such as player age and position. We share information across time and across players by using mixture distributions to control shrinkage for improved accuracy. We co...