Abstract. The contribution of this paper is the adaption of data driven methods for decomposition of tangent shape variability proposed in a probabilistic framework. By Bayesian model selection we compare two generative model representations derived by principal components analysis and by maximum

doi:10.1093/nar/gkn434 A probabilistic generative model for GO enrichment analysis

of voiced and unvoiced regions is derived from a simple probabilistic generative model. Competitive results are presented on pitch tracking, voiced/unvoiced detection and timescale modification; all these tasks and several others can be performed using the single segmentation provided by inference

spectra of voiced and unvoiced regions is derived from a simple probabilistic generative model. Competitiveresults are presented on pitch tracking, voiced/unvoiced detection and timescale modification; all these tasks and sev-eral others can be performed using the single segmentation provided by inference

Abstract—With the growth in the past few years of social tagging services like Delicious and CiteULike, there is growing interest in modeling and mining these social systems for deriving implicit social collective intelligence. In this paper, we propose and explore two probabilistic generative

For the Morpho Challenge 2009 we present an algorithm for unsupervised morphological analysis called Promodes1 which is based on a probabilistic generative model. The model considers segment boundaries as hidden variables and includes probabilities for letter transitions within segments. Promodes

Modern computer systems have become so complex that understanding and predicting the performance of programs is a significant challenge. For instance, when designing microprocessor architectures, engineers must assess the trade-offs involved in allocating the on-die real estate (e.g., between the L1 cache, execution units, etc.) in order to achieve certain performance and power consumption targets. Typically, an experimental hardware architecture is emulated in software, which is extremely computationally inten-sive. Assessing the hardware’s runtime behavior, often referred to as its runtime profile and measured by cycles per instruction executed (CPI) or cache miss rate, requires cycle-accurate simulation at the functional level of the hard-ware. Consequently, researchers have resorted to picking portions of the program execution that are considered typical, called simulation points, and extrapolating from the detailed simulation of these points to the entire runtime profile. State-of-the-art algorithms attempt to strategically select a small set of simulation points that are characteristic of larger portions of program execution by exploiting the phase structure of a program. The idea underlying phase structure is that the execution dynamics of a large program can be understood in terms of a relatively small set of distinct patterns of program behavior. The execution trace is broken into short-duration intervals, and the goal is to assign a phase label to each interval such that the program behavior across all intervals with the same label is similar. Phases are determined from statistics that can be easily and quickly collected as the program executes at the instruction

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desiderata for an effective risk scoring scheme. We propose to use probabilistic generative models for risk scor-ing schemes, and identify several such models, ranging from the simple Naive Bayes, to advanced hierarchical mixture models. Ex-perimental results conducted using real-world datasets show

Probabilistic generative models have been applied successfully in a wide range of applications that range from speech recognition and part of speech tagging, to machine translation and information retrieval, but, traditionally, applications such as reasoning have been thought to fall outside