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MobilityFirst Project Update NSF Meeting March 11, 2013

MobilityFirst Project Update NSF Meeting March 11, 2013. D. Raychaudhuri WINLAB, Rutgers University ray@winlab.rutgers.edu. Introduction : Progress Highlights. MobilityFirst project now moving from design phase to experimental evaluation and GENI/EC2 deployment

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Adaptive annealing: a near-optimal connection between sampling and counting

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COMPSCI 102

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Cooperating Intelligent Systems

Cooperating Intelligent Systems. Bayesian networks Chapter 14, AIMA. Inference. Inference in the statistical setting means computing probabilities for different outcomes to be true given the information

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Introduction to Networks

Introduction to Networks. Sushmita Roy BMI/CS 576 www.biostat.wisc.edu/bmi576 sroy@biostat.wisc.edu Nov 14 th , 2013. Key concepts in networks. What are molecular networks? Different types of networks Algorithms for network reconstruction Classes of methods for network inference

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Predicting Gene Expression using Logic Modeling and Optimization Abhimanyu Krishna

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Recurrence Relation

Recurrence Relation. Outline. What is a recurrence relation ? Solving linear recurrence relations Divide-and-conquer algorithms and recurrence relations. What is a recurrence relation ?. Definition.

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Constraint Satisfaction Problems

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Innovation networks EMB, Hong Kong, 14 September 2005

Innovation networks EMB, Hong Kong, 14 September 2005. Professor David Hargreaves. The challenge and the opportunity. Dynamic governments remain porous. Renewal rarely comes from within. One of the optical illusions of government is that

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Uncertain Observation Times

Uncertain Observation Times. Shaunak Chatterjee & Stuart Russell Computer Science Division University of California, Berkeley. Overview. Why uncertain observation times matter Scenarios considered: Each event is observed: Efficient DP algorithm Missing and false events:

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Chordal Graphs

Chordal Graphs. Bayesian Networks, Winter 2009-2010 Yoav Haimovitch & Ariel Raviv. Contents. Chordal Graph Warm up Theorem 7 Perfect Vertex Elimination Scheme Maximal cliques Tree. Bibliography. M.C.Golumbic Algorithmic Graph Theory and Perfect Graphs. Second edition

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INMARSAT-FLEET 77

INMARSAT-FLEET 77. In response to the ever increasing need for computer-based, cost effective and secure communications system at sea, Inmarsat has developed advance computer solutions that deliver content-rich data and communications solutions to ships of all sizes: INMARSAT Fleet. Fleet F77

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Bayes Nets

Bayes Nets. Introduction to Artificial Intelligence CS440/ECE448 Lecture 19 New homework out today!. Last lecture. Independence and conditional independence Bayes nets This lecture The semantics of Bayes nets Inference with Bayes nets Reading Chapter 14.

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The t test

The t test. Peter Shaw RIl. "Small samples are slippery customers whose word is not to be taken as gospel" (Moroney). Introduction. Last week we met the Mann-Whitney U test, a non-parametric test to examine how likely it is that 2 samples could have come from the same population.

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Lab Assignment 1

Lab Assignment 1. Environments Search Bayes Nets. Problem 1: Peg Solitaire. Is Peg Solitaire: Partially observable? Stochastic? Continuous? Adversarial?. Play online at: http:// www.novelgames.com/flashgames/game.php?id=61 http ://www.gamedesign.jp/flash/peg/peg.html.

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constraint-based problem solving constraint-based problems Generate&Test approach generate candidate solution, then

Dave Reed. constraint-based problem solving constraint-based problems Generate&Test approach generate candidate solution, then test constraints Test&Generate approach apply constraints first, then fill in blanks in candidate timing code: time , doReps. Constraint-based problems.

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Collinearity

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Lecture 8 Confidence interval

Lecture 8 Confidence interval. Parameter and estimate Standard error of the mean (SE) 95% confidence interval Confidence level (coefficient) 1- a Using z score Two sample problem; matched sample problem Illustration with Computer simulation . : population mean, sample mean. Which one? .

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