'Relative entropy' diaporamas de présentation

CS5263 Bioinformatics

CS5263 Bioinformatics. Lecture 18 Motif finding. What is a (biological) motif?. A motif is a recurring fragment, theme or pattern Sequence motif : a sequence pattern of nucleotides in a DNA sequence or amino acids in a protein

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CS5263 Bioinformatics

CS5263 Bioinformatics. Lecture 18 Motif finding. What is a (biological) motif?. A motif is a recurring fragment, theme or pattern Sequence motif : a sequence pattern of nucleotides in a DNA sequence or amino acids in a protein

924 views • 92 slides

Lecture 9 Inexact Theories

Lecture 9 Inexact Theories. Syllabus.

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Lecture 9 Inexact Theories

Lecture 9 Inexact Theories. Syllabus.

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Quantum Unertainty Relations and Some Applications

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EM and variants of HMM Lecture #9

EM and variants of HMM Lecture #9. Background Readings : Chapters 11.2, 11.6, 3.4 in the text book, Biological Sequence Analysis , Durbin et al., 2001. Reminder: Relative Entropy.

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EM algorithm and applications Lecture #9

EM algorithm and applications Lecture #9. Background Readings : Chapters 11.2, 11.6 in the text book, Biological Sequence Analysis , Durbin et al., 2001. Reminder: Relative Entropy.

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Belief in Information Flow

Belief in Information Flow. Michael Clarkson, Andrew Myers, Fred B. Schneider Cornell University 18 th IEEE Computer Security Foundations Workshop June 20, 2005. Password Checker. Some programs require leakage of information. PWC: if p = g then a := 1 else a := 0.

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A variational expression for a generlized relative entropy

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A variational expression for a generlized relative entropy

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Information-Theoretic Measures for Anomaly Detection

Information-Theoretic Measures for Anomaly Detection. Wenke Lee, and Dong Xiang (North Carolina State University). IEEE Security and Privacy, 2001. Speaker: Chang Huan Wu 2009/4/14. Outline. Introduction Information-Theoretic Measures Case Studies Conclusions. Introduction (1/2).

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Benchmarking Anomaly-based Detection Systems

Benchmarking Anomaly-based Detection Systems. Ashish Gupta Network Security May 2004. Overview. The Motivation for this paper Waldo example The approach Structure in data Generating the data and anomalies Injecting anomalies Results Training and Testing: the method Scoring

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The Relative Entropy Rate of Two Hidden Markov Processes

The Relative Entropy Rate of Two Hidden Markov Processes. Or Zuk Dept. of Phys. Of Comp. Systems Weizmann Inst. Of Science Rehovot, Israel. Overview. Introduction Distance Measures and Relative Entropy rate Results: Generalization from Entropy Rate. Future Directions. 9.0. 8.0.

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The Relative Entropy Rate of Two Hidden Markov Processes

The Relative Entropy Rate of Two Hidden Markov Processes. Or Zuk Dept. of Phys. Of Comp. Systems Weizmann Inst. Of Science Rehovot, Israel. Overview. Introduction Distance Measures and Relative Entropy rate Results: Generalization from Entropy Rate. Future Directions. 9.0. 8.0.

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Language Modeling Again

Language Modeling Again. So are we smooth now? Courtesy of Chris Jordan. So what did we talk about last week?. Language models represent documents as multinomial distributions What is a multinomial? The Maximum Likelihood Estimate calculates document model

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Motifs for Unknown Sites

Motifs for Unknown Sites. Vasileios Hatzivassiloglou University of Texas at Dallas. Decomposing KL. Back to our profiles If we consider the independent marginal distributions P j and Q j for each of the n positions j , it can be shown that

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Venkatarajan S. Mathura et al. Presented by Mr. Hat

Identifying property based sequence motifs in protein families and superfamies: application to DNase-1 related endonucleases. Venkatarajan S. Mathura et al. Presented by Mr. Hat. Motivation.

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Identifying functional residues of proteins from sequence info

Identifying functional residues of proteins from sequence info Using MSA (multiple sequence alignment) - search for remote homologs using HMMs or profiles Remote homologs with no known structure Given a large, diverse superfamily protein may evolve different function or subtype

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Identifying functional residues of proteins from sequence info

Identifying functional residues of proteins from sequence info Using MSA (multiple sequence alignment) - search for remote homologs using HMMs or profiles Remote homologs with no known structure Given a large, diverse superfamily protein may evolve different function or subtype

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SATCHMO: Simultaneous Alignment and Tree Construction using Hidden Markov mOdels

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