'Asymptotic behavior' diaporamas de présentation

Learning Bayesian Networks from Data

Learning Bayesian Networks from Data. Nir Friedman Daphne Koller Hebrew U. Stanford. Overview. Introduction Parameter Estimation Model Selection Structure Discovery Incomplete Data Learning from Structured Data. Qualitative part :

1.31k views • 113 slides

Summing Up All Genus Free Energy of ABJM Matrix Model

Summing Up All Genus Free Energy of ABJM Matrix Model. Sanefumi Moriyama (Nagoya U) JHEP [arXiv:1106.4631] with H.Fuji and S.Hirano. M is for Mother. 5 Consistent String Theories in 10D. Het-E8xE8. IIA. Het-SO(32). IIB. I. M is for Mother. 5 Consistent String Theories in 10D.

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Nonlinear Dynamics of Iterative Decoding Systems: Analysis and Applications

Nonlinear Dynamics of Iterative Decoding Systems: Analysis and Applications . Ljupco Kocarev University of California, San Diego. Summary of the presentation. Nonlinear Dynamics of the iterative coding systems Nonlinear Codes with Latin Squares (Quasi-groups) Conclusions .

750 views • 49 slides

Learning Progressions for the Common Core State Standards

Learning Progressions for the Common Core State Standards. Bradford Findell April 15, 2011 NCTM Annual Meeting Brad.Findell@ode.state.oh.us. Association of State Supervisors of Mathematics. Grade Level Overview. Cross-cutting themes . Critical Area of Focus. Format of K-8 Standards.

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Condensation

Condensation. Reading: Chap 13. In the continuum regime, diffusion theory is used. At steady state. Q: Is n d equal to n s ?. Rate of diffusional condensation. Q: How much is it?. Volume growth. Size growth. Q: How can the condensation rate be increased?

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Power Management in Data Centers: Theory & Practice

Power Management in Data Centers: Theory & Practice . Mor Harchol -Balter Computer Science Dept Carnegie Mellon University. Anshul Gandhi, Sherwin Doroudi , Alan Scheller -Wolf, Mike Kozuch. Annual U.S. data center energy consumption.

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Theoretical Aspects of Dark Energy

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Power Functions

Power Functions. Lesson 9.1. Power Function. Definition Where k and p are constants Power functions are seen when dealing with areas and volumes Power functions also show up in gravitation (falling bodies). Direct Proportions.

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Harmonic Oscillator

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Performance of DFT Sergio Aragon San Francisco State University CalTech PASI January 4-16, 2004

Performance of DFT Sergio Aragon San Francisco State University CalTech PASI January 4-16, 2004. H 2 Dissociation Curve. Performance: Ozone. Bond lengths & Bond Angles. Koch & Holthausen. Octahedral Cr Complex. Octahedral Metallic Carbonyls. Vibrational Frequencies. Thermochemistry.

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Noncommutative Deformation of InstantonS , Instanton numbers and ADHM construction

Noncommutative Deformation of InstantonS , Instanton numbers and ADHM construction. ICMP 09, Prague, August 3, 2009. Akifumi Sako Kushiro National College of Technology. NC parameter , Comm. Lim. , Moyal Product. NC parameter , ? ? 0 comm. lim. . Moyal product.

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Fundamentals

Fundamentals. CSE 326 Data Structures Lecture 2. T A. Run Time. T B. Input Size N. Motivation for Algorithm Analysis. Suppose you are given two algorithms A and B for solving a problem The running times T A (N) and T B (N) of A and B as a function of input size N are given.

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Master Theorem

Master Theorem. Section 7.3 of Rosen Fall 2008 CSCE 235 Introduction to Discrete Structures Course web-page: cse.unl.edu/~cse235 Questions : cse235@cse.unl.edu. Outline. Motivation The Master Theorem Pitfalls 3 examples 4 th Condition 1 example.

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Master Theorem

Master Theorem. Section 7.3 of Rosen Fall 2010 CSCE 235 Introduction to Discrete Structures Course web-page: cse.unl.edu/~cse235 Questions : cse235@cse.unl.edu. Outline. Motivation The Master Theorem Pitfalls 3 examples 4 th Condition 1 example.

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Master Theorem

Master Theorem. Section 8.3 of Rosen Spring 2013 CSCE 235 Introduction to Discrete Structures Course web-page: cse.unl.edu/~cse235 Questions : Piazza. Outline. Motivation The Master Theorem Pitfalls 3 examples 4 th Condition 1 example.

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93 views • 9 slides