We examine the usefulness of Bayesian statistical methods for the modeling of biochemical reactions. With simulated data, it is shown that these methods can effectively fit mechanistic models of sequences of enzymatic reactions to experimental data. These methods have the advantages of being relatively easy to use and producing probability distributions for the model parameters rather than poin...

As IC technologies scale to finer feature sizes, it becomes increasingly difficult to control the relative process variations. The increasing fluctuations in manufacturing processes have introduced unavoidable and significant uncertainty in circuit performance; hence ensuring manufacturability has been identified as one of the top priorities of today’s IC design problems. In this paper, we revi...

We present context-sensitive dynamic classes – a novel mechanism for integrating contextual information from spoken dialogue into a class n-gram language model. We exploit the dialogue system’s information state to populate dynamic classes, thus percolating contextual constraints to the recognizer’s language model in real time. We describe a technique for training a language model incorporating...

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...

The demand of intelligent machines that may recognize the spoken speech and respond in a natural voice has been driving speech research. The challenging in speech recognition systems due to the language nature where there are no clear boundaries between words, the phonetic beginning and ending are influenced by neighbouring words, in addition to the variability in different speakers speech: mal...

Statistical model results may strongly contradict domain knowledge or expectation, which generates many problems considered in this article. In a nutshell, who is right: a statistician stating that advertising does not work (based on the model), or a marketing officer stating the opposite (based on experience or “gut feeling”)? The answer is not as obvious as it seems. All statisticians face th...

Until now, writing has been described in the research literature as the interplay of situations, strategies and phases – with phases being identifiable temporal procedural units with typical dominant writing actions such as ‘formulating’ or ‘source reading’. Phases are recognized as essential for the success of writing processes. At the same time, most scientific approaches to writing base thei...

Corpus based Natural Language Processing (NLP) is now a well established paradigm in NLP. The availability of large corpora, often annotated in various way has led to the development of a variety of approaches to statistical language modeling. The papers in this session represent many of these important approaches. I will try to classify these papers along different dimensions, thus providing t...

In this paper we describe a real-time computer vision and machine learning system for modeling and recognizing human behaviors in a visual surveillance task. The system is particularly concerned with detecting when interactions between people occur, and classifying the type of interaction. Examples of interesting interaction behaviors include following another person, altering one's path to mee...

Images of natural scenes contain a rich variety of visual patterns. To learn and recognize these patterns from natural images, it is necessary to construct statistical models for these patterns. In this review article we describe three statistical principles for modeling image patterns: the sparse coding principle, the minimax entropy principle, and the meaningful alignment principle. We explai...