Exploring the growing interest in extending the theory of probability and statistics to allow for more flexible modeling of uncertainty, ignorance, and fuzziness, the properties of fuzzy modeling are investigated for statistical signals, which benefit from the properties of fuzzy modeling. There is relatively research in the area, making explicit identification of statistical/stochastic fuzzy m...

In this work we investigate aperiodicities that occur in the sustained portion of a sound due to phase synchronous versus unsynchronous deviations of the partials. By using an additive sinusoidal model, correlation statistics and phase coupling eeects are considered for various instruments. The phase coupling is shown to be an important characteristic of musical instrument. These analyses are c...

Prediction, i.e., predicting the potential values or value distributions of certain attributes for objects in a database or data warehouse, is an attractive goal in data mining. To predict future events not shown in databases with high quality can help users to make smart business decisions. With the concern of both scalability and high quality of prediction, we propose a predictive modeling al...

This article introduces a mathematical approach to extracting some statistical parameters from noise-like sounds. This approach could define a new spectral model or extend existing ones to analyze and synthesize such complex sounds. In the future, this method could also permit electro-acoustic composers to make musical transformations. We have performed several synthesis tests with synthetic an...

Recent results on sparse coding and independent component analysis suggest that human vision first represents a visual image by a linear superposition of a relatively small number of localized, elongate, oriented image bases. With this representation, the sketch of an image consists of the locations, orientations, and elongations of the image bases, and the sketch can be visually illustrated by...

Statistical modeling of data sets by neural-network techniques is offered as an alternative to traditional semiempirical approaches to global modeling of nuclear properties. New results are presented to support the position that such novel techniques can rival conventional theory in predictive power, if not in economy of description. Examples include the statistical inference of atomic masses a...

Over the past several years, the successful application of statistical techniques in natural language processing has penetrated further and further into written language technology, proceding with time from the periphery of written language processing into deeper and deeper aspects of language processing. At the periphery of natural language understanding, Hidden Markov Models were first applie...

Formalisms for specifying general statistical models, such as probabilistic-programming languages, typically consist of two components: a specification of a stochastic process (the prior), and a specification of observations that restrict the probability space to a conditional subspace (the posterior). Use cases of such formalisms include the development of algorithms in machine learning and ar...

Robust, high yield IC design requires statistical simulation, and therefore statistical models. Simple “fast” and “slow” sets of model parameters are not sufficient to predict the manufacturing variations of all measures of circuit performance for arbitrary circuit topologies, device geometries, and biases. This paper describes an accurate and efficient approach to statistical modeling and char...