PROBABILISTIC ROBOTICS Sebastian THRUN Stanford University Stanford, CA Wolfram BURGARD University of Freiburg Freiburg, Germany Dieter FOX University of Washington Seattle,…
Slide 1 1 The Monte Carlo method Slide 2 2 (0,0) (1,1) (-1,-1) (-1,1) (1,-1) 1 Z= 1 If X 2 +Y 2 1 0 o/w (X,Y) is a point chosen uniformly at random in a 2 2 square…
Slide 1 Rethinking NetFlow: A Case for a Coordinated “RISC” Architecture for Flow Monitoring Vyas Sekar Joint work with Mike Reiter, Hui Zhang David Andersen, Anupam…
Slide 1 1 Algorithms for Large Data Sets Ziv Bar-Yossef Lecture 10 June 4, 2006 http://www.ee.technion.ac.il/courses/049011 Slide 2 2 Random Sampling of Web Pages Slide 3…
Slide 1 1 Approximate Inference 2: Monte Carlo Markov Chain Slide 2 MCMC Limitations of LW: Evidence affects sampling only for nodes that are its descendants For nondescendants,…
Modeling Documents by Combining Semantic Concepts with Unsupervised Statistical Learning Author: Chaitanya Chemudugunta America Holloway Padhraic Smyth Mark Steyvers Source:…
The Monte Carlo method (X,Y) is a point chosen uniformly at random in a 22 square centered at the origin (0,0). P(Z=1)=/4. Assume we run this experiment m times,…