'Indicator function' presentation slideshows

Searching for Structure in Geometry Processing

Searching for Structure in Geometry Processing. Misha Kazhdan. What do we want to compute? (What established theory can we apply?) How do we compute it effectively? (How do we implement efficiently/robustly?). Geometry Processing in Detail. Symmetry Detection Discrete Continuous

636 views • 54 slides

A brief maximum entropy tutorial

A brief maximum entropy tutorial. Overview. Statistical modeling addresses the problem of modeling the behavior of a random process

691 views • 39 slides

Dynamic percolation, exceptional times, and harmonic analysis of boolean functions

Dynamic percolation, exceptional times, and harmonic analysis of boolean functions. Oded Schramm. joint w/ Jeff Steif. Percolation. Dynamic Percolation. Infinite clusters?. Harris (1960): There is no infinite cluster Kesten (1980): There is if we increase p. For static percolation:.

578 views • 34 slides

Simulations of solidification microstructures by the phase-field method

621 views • 25 slides

Generating Daily Changes in Market Variables using Multivariate Mixture-of-Normals

Generating Daily Changes in Market Variables using Multivariate Mixture-of-Normals. Jin Wang Valdosta State University Michael R. Taaffe University of Minnesota December 10, 2001 Winter Simulation Conference 2001. Outline. Introduction Fat Tails and Skewness

434 views • 22 slides

The Improved Iterative Scaling Algorithm: A gentle Introduction

The Improved Iterative Scaling Algorithm: A gentle Introduction. Adam Berger, CMU, 1997. Introduction. Random process Produces some output value y , a member of a (necessarily finite) set of possible output values

749 views • 20 slides

Theorem

Theorem. For g : R ? R If X is a discrete random variable then If X is a continuous random variable Proof: We proof it for the discrete case. Let Y = g ( X ) then. Example to illustrate steps in proof. Suppose i.e. and the possible values of X are

569 views • 20 slides

We have a TA!

196 views • 13 slides

Community Structure In Time-Dependent, Multiscale, And Multiplex Networks

Community Structure In Time-Dependent, Multiscale, And Multiplex Networks. Peter J. Mucha, Thomas Richardson, Kevin Macon, Mason A. Porter, Jukka-Pekka Onnela. Science 14 May 2010: Vol. 328. no. 5980, pp. 876 - 878 DOI: 10.1126/science.1184819. Standard Evaluation of Communities.

233 views • 7 slides