Musical signals can be well represented as a sum of harmonically related sinusoids motivated by consideration of the sound generation mechanisms of many musical instruments. We consider the problem of estimating the harmonic model parameters in a Bayesian framework which has the potential to incorporate a priori knowledge about the structure of the data and of its parameters. Constraints can be...

We are interested in the generation of distinguishing place or route descriptions for urban environments. Such descriptions require a hierarchical model of the discourse, the elements of the city. We postulate that cognitive hierarchies, as used in human communication, can be sufficiently reflected in machine-generated hierarchies. In this paper we (a) propose a computational model for the gene...

This paper employs a three-level hierarchical linear model (HLM) to examine the impacts that the quality of the environment and neighbourhood affluence have on housing prices. The empirical results suggest that there are significant variations in the average housing price for different neighbourhoods and administrative districts. The impact of building characteristics on housing prices is subje...

• Count data are often modeled using a Poisson model. • If y ∼ Poisson(μ) then E(y) = var(y) = μ. • When counts are assumed exchangeable given μ and the rates μ can also be assumed to be exchangeable, a Gamma population model for the rates is often chosen. • The hierarchical model is then yi ∼ Poisson(μi) μi ∼ Gamma(α, β). • Priors for the hyperparameters are often taken to be Gamma (or exponen...

• Count data are often modeled using a Poisson model. • If y ∼ Poisson(μ) then E(y) = var(y) = μ. • When counts are assumed exchangeable given μ and the rates μ can also be assumed to be exchangeable, a Gamma population model for the rates is often chosen. • The hierarchical model is then yi ∼ Poisson(μi) μi ∼ Gamma(α, β). • Priors for the hyperparameters are often taken to be Gamma (or exponen...

Gaussian mixture models (GMMs) are a convenient and essential tool for the estimation of probability density functions. Although GMMs are used in many research domains from image processing to machine learning, this statistical mixture modeling is usually complex and further needs to be simplified. In this paper, we present a GMM simplification method based on a hierarchical clustering algorith...

Topic Detection and Tracking (TDT) is a variant of classiication in which the classes are not known or xed in advance. Consider for example an incoming stream of news articles or email messages that are to be classiied by topic; new classes must be created as new topics arise. The problem is a challenging one for machine learning. Instances of new topics must be recognized as not belonging to a...

– A hierarchical fuzzy model is proposed in this paper. The concept of relevance has enabled the measurement of the relative importance of rule sets and the Separation of Linguistic Information Methodology (SLIM) provided a means to organize its information in different structures. Based on this methodology a new SLIM-PCS algorithm is proposed for the Parallel Collaborative Structure (PCS). As ...

We present an approach to model-based hi­ erarchical clustering by formulating an ob­ jective function based on a Bayesian anal­ ysis. This model organizes the data into a cluster hierarchy while specifying a complex feature-set partitioning that is a key compo­ nent of our model. Features can have either a unique distribution in every cluster or a com­ mon distribution over some (or even all) ...

Model-based reasoning about a system requires an explicit representation of the system's components and their connections. Diagnosing such a system consists of locating those components whose abnormal behavior accounts for the faulty system behavior. In order to increase the eeciency of model-based diagnosis, we propose a model representation at several levels of detail , and deene three reenem...