Paul H. Garthwaite

Learn More
We consider the problem of intra-opus pattern discovery, that is, the task of discovering patterns of a specified type within a piece of music. A music analyst undertook this task for works by Domenico Scarlattti and Johann Sebas-tian Bach, forming a benchmark of 'target' patterns. The performance of two existing algorithms and one of our own creation,(More)
Stylistic composition is a creative musical activity, in which students as well as renowned composers write according to the style of another composer or period. We describe and evaluate two computational models of stylistic composition, called Racchman-Oct2010 and Racchmaninof-Oct2010. The former is a constrained Markov model and the latter embeds this(More)
This paper proposes a model for term re-occurrence in a text collection based on the gaps between successive occurrences of a term. These gaps are modeled using a mixture of exponential distributions. Parameter estimation is based on a Bayesian framework that allows us to fit a flexible model. The model provides measures of a term's re-occurrence rate and(More)
We motivate the need for dataset profiling in the context of evaluation, and show that textual datasets differ in ways that challenge assumptions about the applicability of techniques. We set out some criteria for useful profiling measures. We argue that distribution patterns of frequent words are useful in profiling genre, and report on a series of(More)
Quadratic forms capture multivariate information in a single number, making them useful, for example, in hypothesis testing. When a quadratic form is large and hence interesting, it might be informative to partition the quadratic form into contributions of individual variables. In this paper it is argued that meaningful partitions can be formed, though the(More)
A new method is proposed for the correction of confidence intervals when the original interval does not have the correct nominal coverage probabilities in the frequentist sense. The proposed method is general and does not require any distributional assumptions. It can be applied to both frequentist and Bayesian inference where interval estimates are(More)
  • 1