'Power laws' presentation slideshows

MAD-Bayes: MAP-based Asymptotic Derivations from Bayes

MAD-Bayes: MAP-based Asymptotic Derivations from Bayes. Michael I. Jordan INRIA University of California, Berkeley. May 11, 2013. Acknowledgments : Brian Kulis , Tamara Broderick. Statistical Inference and Big Data.

839 views • 66 slides

Complex Adaptive Systems of Systems (CASoS) Modeling and Engineering

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652 views • 49 slides

Diversity of Graphs with Highly Variable Connectivity*

Diversity of Graphs with Highly Variable Connectivity*. David Alderson Operations Research Department Naval Postgraduate School *Joint work with Lun Li (Caltech) Acknowledgments: John Doyle, Walter Willinger, Daniel Whitney IPAM Workshop: Random and Dynamic Graphs and Networks May 8, 2007.

643 views • 48 slides

A History of and New Directions for Power Law Research

A History of and New Directions for Power Law Research. Michael Mitzenmacher Harvard University. Warning. This talk does not have specific new results. Survey of past and present. Meant to be provocative and inspire future research directions. Motivation: General.

662 views • 46 slides

Ising Models, Statistical Mechanics, and Critical Phenomenon

Ising Models, Statistical Mechanics, and Critical Phenomenon. Original 1-Dimensional Ising Model. Ferromagnetic. Or. Antiferromagnetic. Paramagnetic. Hamiltonian. Represents the total Energy in the System Classically this is usually kinetic and potential energy

1.51k views • 43 slides

563 views • 43 slides

The Drum

The Drum. Signal Fires. Smoke Signals. Signal Mirrors. Signal Flags. Can & String. Telegraph. Telephone. ISDN Digital Telephone. Satellite Telephone. Digital Cellular Phone. Digital Network Cell-phone. Torsion Field Communications. The Basics. How Nature Works

634 views • 36 slides

The retrieval of the LWC in water clouds

The retrieval of the LWC in water clouds. O. A. Krasnov and H. W. J. Russchenberg International Research Centre for Telecommunications-transmission and Radar, Faculty of Information Technology and Systems, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands.

503 views • 34 slides

CS 345A Data Mining Lecture 1

CS 345A Data Mining Lecture 1. Introduction to Web Mining. What is Web Mining?. Discovering useful information from the World-Wide Web and its usage patterns. Web Mining v. Data Mining. Structure (or lack of it) Textual information and linkage structure Scale

565 views • 29 slides

Why Stock Markets Crash

460 views • 29 slides

Algorithmic Problems in the Internet

Algorithmic Problems in the Internet. Christos H. Papadimitriou www.cs.berkeley.edu/~christos. Goals of TCS (1950-2000):

336 views • 26 slides

Logarithm and Exponential Functions

1.05k views • 24 slides

Satic Implicit Static Explicit Dynamic Explicit

Possible simulation techniques in metal forming. Satic Implicit Static Explicit Dynamic Explicit. Smooth Particle Hydrodynamics Element Free Galerkin Method Moving Least Square Method Approximation Reproducing Kernel Particle Method. Modular Structure of Forming Processes.

342 views • 24 slides

Small World: decentralized search

337 views • 24 slides

Satic Implicit Static Explicit Dynamic Explicit

Possible simulation techniques in metal forming. Satic Implicit Static Explicit Dynamic Explicit. Smooth Particle Hydrodynamics Element Free Galerkin Method Moving Least Square Method Approximation Reproducing Kernel Particle Method. Modular Structure of Forming Processes.

444 views • 23 slides

409 views • 22 slides

Introduction to e-Commerce

331 views • 13 slides

Chapter 8: Data-Driven Models

496 views • 12 slides

Web Science: AnalyZing the Web

Web Science: AnalyZing the Web. Graph Terminology. Graph ~ a structure of nodes/vertices connected by edges The edges may be directed or undirected Distance ~ shortest # of edges connecting 2 vertices Diameter ~ greatest distance b/w any 2 vertices of a graph.

203 views • 8 slides