'Probability 1 2' presentation slideshows

Probability and Discrete Random Variable

Probability and Discrete Random Variable. Probability. What is Probability?. When we talk about probability , we are talking about a (mathematical) measure of how likely it is for some particular thing to happen Probability deals with chance behavior

1.86k views • 131 slides

Discrete Probability

Discrete Probability. CSC-2259 Discrete Structures. Introduction to Discrete Probability. Unbiased die. Sample Space:. All possible outcomes. Event:. any subset of sample space. Experiment:. procedure that yields events. Throw die. Probability of event :. Note that:. since.

1.06k views • 83 slides

Discrete Probability

Discrete Probability. CSC-2259 Discrete Structures. Introduction to Discrete Probability. Unbiased die. Sample Space:. All possible outcomes. Event:. any subset of sample space. Experiment:. procedure that yields events. Throw die. Probability of event :. Note that:. since.

847 views • 83 slides

CSCE 411 Design and Analysis of Algorithms

CSCE 411 Design and Analysis of Algorithms. Set 9: Randomized Algorithms Slides by Prof. Jennifer Welch Spring 2014. The Hiring Problem. You need to hire a new employee. The headhunter sends you a different applicant every day for n days.

813 views • 54 slides

Saurabh Singal

577 views • 48 slides

A Quantum Local Lemma

452 views • 45 slides

How to compile searching software so that it is impossible to reverse-engineer.

How to compile searching software so that it is impossible to reverse-engineer. (Private Keyword Search on Streaming Data). Rafail Ostrovsky William Skeith UCLA. (patent pending). Airport 2 passenger list. Airport 3 passenger list. Airport 1 passenger list.

383 views • 38 slides

How to compile searching software so that it is impossible to reverse-engineer.

How to compile searching software so that it is impossible to reverse-engineer. (Private Keyword Search on Streaming Data). Rafail Ostrovsky William Skeith UCLA. (patent pending). Airport 2 passenger list. Airport 3 passenger list. Airport 1 passenger list.

384 views • 38 slides

Random walks on undirected graphs and a little bit about Markov Chains

Random walks on undirected graphs and a little bit about Markov Chains. Guy. One dimensional Random walk. A random walk on a line. A line is. n. 0. 1. 2. 3. 4. 5. If the walk is on 0 it goes into 1 . Else it goes to i+1 or to i-1 with probability 1/2.

345 views • 34 slides

Introduction to decision analysis

Introduction to decision analysis. Jouni Tuomisto THL. Q R A. Decision analysis is done for purpose: to inform and thus improve action. Decisions by an individual vs. in a society.

418 views • 32 slides

Introduction to decision analysis

Introduction to decision analysis. Jouni Tuomisto THL. Q R A. Decision analysis is done for purpose: to inform and thus improve action. Decisions by an individual vs. in a society.

355 views • 32 slides

Algorithms for massive data sets

Algorithms for massive data sets. Lecture 2 (Mar 14, 2004) Yossi Matias & Ely Porat (partially based on various presentations & notes). Negative Result for Sampling [ Charikar, Chaudhuri, Motwani, Narasayya 2000 ].

391 views • 31 slides

4. Random variables part one

4. Random variables part one. Random variable. A discrete random variable assigns a discrete value to every outcome in the sample space. Example. { HH , HT , TH , TT }. N = number of H s. Probability mass function.

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ST3236: Stochastic Process Tutorial 8

ST3236: Stochastic Process Tutorial 8. TA: Mar Choong Hock Email: g0301492@nus.edu.sg Exercises: 9. Question 1. A population begins with a single individual. In each generation, each individual in the population dies with probability 0.5 or double with probability 0.5.

559 views • 17 slides

Richard Cleve DC 2117 cleve@cs.uwaterloo

Introduction to Quantum Information Processing CS 467 / CS 667 Phys 667 / Phys 767 C&O 481 / C&O 681. Lecture 11 (2011). Richard Cleve DC 2117 cleve@cs.uwaterloo.ca. Order-finding via eigenvalue estimation. Example: Z 21 * = { 1,2,4,5,8,10,11,13,16,17,19,20 }.

277 views • 15 slides

Probability and Sample space

Probability and Sample space. We call a phenomenon random if individual outcomes are uncertain but there is a regular distribution of outcomes in a large number of repetitions.

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Uncertainty

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Dimensionality Reduction

Dimensionality Reduction. Random Projections. Johnson-Lindenstrauss lemma For: 0< e < 1/2, any (sufficiently large) set S of M points in R n k = O( e -2 lnM) There exists a linear map f: S ? R k , such that (1- e ) ||u-v|| 2 ? ||f(u)-f(v)|| 2 ? (1+ e )||u-v|| 2 for u,v in S

193 views • 8 slides

Selection

257 views • 8 slides

Selection

190 views • 8 slides