• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Tools

Sorted by:
Try your query at:
Semantic Scholar Scholar Academic
Google Bing DBLP
Results 1 - 10 of 801
Next 10 →

R: RNA sequence analysis using covariance models. Nucleic Aeids Res

by Sean R. Eddy, Richard Durbin , 1994
"... We describe a general approach to several RNA sequence analysis problems using probabilistic models that flexibly describe the secondary structure and primary sequence consensus of an RNA sequence family. We call these models 'covariance models'. A covariance model of tRNA sequences is an ..."
Abstract - Cited by 367 (9 self) - Add to MetaCart
We describe a general approach to several RNA sequence analysis problems using probabilistic models that flexibly describe the secondary structure and primary sequence consensus of an RNA sequence family. We call these models 'covariance models'. A covariance model of tRNA sequences

PRISM: Probabilistic symbolic model checker

by Marta Kwiatkowska, Gethin Norman, David Parker , 2002
"... Abstract. In this paper we describe PRISM, a tool being developed at the University of Birmingham for the analysis of probabilistic systems. PRISM supports two probabilistic models: continuous-time Markov chains and Markov decision processes. Analysis is performed through model checking such systems ..."
Abstract - Cited by 236 (13 self) - Add to MetaCart
Abstract. In this paper we describe PRISM, a tool being developed at the University of Birmingham for the analysis of probabilistic systems. PRISM supports two probabilistic models: continuous-time Markov chains and Markov decision processes. Analysis is performed through model checking

Progressivemauve: multiple genome alignment with gene gain, loss and rearrangement

by Aaron E. Darling, Bob Mau, Nicole T. Perna - Article ID e11147 , 2010
"... Background: Multiple genome alignment remains a challenging problem. Effects of recombination including rearrangement, segmental duplication, gain, and loss can create a mosaic pattern of homology even among closely related organisms. Methodology/Principal Findings: We describe a new method to align ..."
Abstract - Cited by 272 (3 self) - Add to MetaCart
. The method uses a novel alignment objective score called a sum-of-pairs breakpoint score, which facilitates accurate detection of rearrangement breakpoints when genomes have unequal gene content. We also apply a probabilistic alignment filtering method to remove erroneous alignments of unrelated sequences

Reading Tea Leaves: How Humans Interpret Topic Models

by Jonathan Chang, Jordan Boyd-graber, Sean Gerrish, Chong Wang, David M. Blei
"... Probabilistic topic models are a popular tool for the unsupervised analysis of text, providing both a predictive model of future text and a latent topic representation of the corpus. Practitioners typically assume that the latent space is semantically meaningful. It is used to check models, summariz ..."
Abstract - Cited by 238 (26 self) - Add to MetaCart
Probabilistic topic models are a popular tool for the unsupervised analysis of text, providing both a predictive model of future text and a latent topic representation of the corpus. Practitioners typically assume that the latent space is semantically meaningful. It is used to check models

High-quality Motion Deblurring from a Single Image

by Qi Shan, Jiaya Jia, Aseem Agarwala , 2008
"... Figure 1 High quality single image motion-deblurring. The left sub-figure shows one captured image using a hand-held camera under dim light. It is severely blurred by an unknown kernel. The right sub-figure shows our deblurred image result computed by estimating both the blur kernel and the unblurre ..."
Abstract - Cited by 184 (6 self) - Add to MetaCart
restoration. We present an analysis of the causes of common artifacts found in current deblurring methods, and then introduce several novel terms within this probabilistic model that are inspired by our analysis. These terms include a model of the spatial randomness of noise in the blurred image, as well a

A Probabilistic Poly-Time Framework for Protocol Analysis

by P. Lincoln, J. Mitchell, M. Mitchell, A. Scedrov , 1998
"... We develop a framework for analyzing security protocols in which protocol adversaries may be arbitrary probabilistic polynomial-time processes. In this framework, protocols are written in a form of process calculus where security may be expressed in terms of observational equivalence, a standard rel ..."
Abstract - Cited by 114 (6 self) - Add to MetaCart
We develop a framework for analyzing security protocols in which protocol adversaries may be arbitrary probabilistic polynomial-time processes. In this framework, protocols are written in a form of process calculus where security may be expressed in terms of observational equivalence, a standard

Sampling-based algorithms for optimal motion planning

by Sertac Karaman, Emilio Frazzoli - International Journal of Robotics Research (IJRR
"... During the last decade, sampling-based path planning algorithms, such as Probabilistic RoadMaps (PRM) and Rapidly-exploring Random Trees (RRT), have been shown to work well in practice and possess theoretical guarantees such as probabilistic completeness. However, little effort has been devoted to t ..."
Abstract - Cited by 187 (14 self) - Add to MetaCart
to the formal analysis of the quality of the solution returned by such algorithms, e.g., as a function of the number of samples. The purpose of this paper is to fill this gap, by rigorously analyzing the asymptotic behavior of the cost of the solution returned by stochastic sampling-based algorithms

A probabilistic polynomial-time calculus for analysis of cryptographic protocols

by John C. Mitchell, Ajith Ramanathan, Andre Scedrov, Vanessa Teague - Electronic Notes in Theoretical Computer Science , 2001
"... We prove properties of a process calculus that is designed for analyzing security protocols. Our long-term goal is to develop a form of protocol analysis, consistent with standard cryptographic assumptions, that provides a language for expressing probabilistic polynomial-time protocol steps, a spec ..."
Abstract - Cited by 48 (8 self) - Add to MetaCart
We prove properties of a process calculus that is designed for analyzing security protocols. Our long-term goal is to develop a form of protocol analysis, consistent with standard cryptographic assumptions, that provides a language for expressing probabilistic polynomial-time protocol steps, a

SoRec: Social Recommendation Using Probabilistic Matrix Factorization

by Hao Ma, Haixuan Yang, Michael R. Lyu, Irwin King , 2008
"... Data sparsity, scalability and prediction quality have been recognized as the three most crucial challenges that every collaborative filtering algorithm or recommender system confronts. Many existing approaches to recommender systems can neither handle very large datasets nor easily deal with users ..."
Abstract - Cited by 126 (8 self) - Add to MetaCart
by online social networks, social network analysis is becoming important for many Web applications. Following the intuition that a person’s social network will affect personal behaviors on the Web, this paper proposes a factor analysis approach based on probabilistic matrix factorization to solve the data

Server Models for Probabilistic Network Calculus

by Markus Fidler
"... Abstract: Network calculus is a deterministic queuing theory that has gained increas-ing attention in recent time. Founded on min-plus algebra it resorts to intuitive con-volution formulae for efficient concatenation of servers and derivation of related per-formance bounds. Yet, the pessimistic wors ..."
Abstract - Add to MetaCart
worst-case analysis of deterministic network calculus gave rise to probabilistic counterparts that aim at utilizing the smoothing ef-fects of statistical multiplexing by allowing for certain violation probabilities. Related theories are, however, significantly more complicated and still subject
Next 10 →
Results 1 - 10 of 801
Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University