Randomized Algorithms Randomized Algorithms CS648 Lecture 9 Random Sampling part-I (Approximating a parameter) Lecture 9 Random Sampling part-I (Approximating.

Slides:



Advertisements
Similar presentations
Introduction to Monte Carlo Markov chain (MCMC) methods
Advertisements

2 4 Theorem:Proof: What shall we do for an undirected graph?
Size-estimation framework with applications to transitive closure and reachability Presented by Maxim Kalaev Edith Cohen AT&T Bell Labs 1996.
Randomized Algorithms Randomized Algorithms CS648 Lecture 2 Randomized Algorithm for Approximate Median Elementary Probability theory Lecture 2 Randomized.
Randomized Algorithms Randomized Algorithms CS648 Lecture 14 Expected duration of a randomized experiment Part II Lecture 14 Expected duration of a randomized.
Randomized Algorithms Randomized Algorithms CS648 Lecture 20 Probabilistic Method (part 1) Lecture 20 Probabilistic Method (part 1) 1.
Foundations of Computer Graphics (Fall 2012) CS 184, Lecture 11: Curves Problems
Randomized Algorithms Randomized Algorithms CS648 Lecture 6 Reviewing the last 3 lectures Application of Fingerprinting Techniques 1-dimensional Pattern.
Graduate School of Information Sciences, Tohoku University
CS8803-NS Network Science Fall 2013 Instructor: Constantine Dovrolis
Randomized Algorithms Randomized Algorithms CS648 Lecture 8 Tools for bounding deviation of a random variable Markov’s Inequality Chernoff Bound Lecture.
Review for Midterm Including response to student’s questions Feb 26.
Sam Pfister, Stergios Roumeliotis, Joel Burdick
Motion Tracking. Image Processing and Computer Vision: 82 Introduction Finding how objects have moved in an image sequence Movement in space Movement.
A Las Vegas Algorithm for the 8 Queens problem
Course overview Tuesday lecture –Those not presenting turn in short review of a paper using the method being discussed Thursday computer lab –Turn in short.
Lecture 4 Measurement Accuracy and Statistical Variation.
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Course outline and schedule Introduction (Sec )
6-5 The Central Limit Theorem
VAVLPVCTYMAUS PSABLADDERZSB EBSANTESHTICL RLDUDSKTTVSRA EDEARCENEAUOD CRFNORSASINTD TPEUUOCPTDATP UNRTMTRBEEXME MIEUSUULSNSNN USNMEMNISAIIT AESXSVPENNISI.
Absolute error. absolute function absolute value.
QPLNHTURBIOTS CADAIASOINCOS OSTPOSTLGVAGT AJRLFKLEROUEA CLARITYSOLSTB HTEAMVSRUVAHI INTERACTPELEL NAPKSOCIALIRI GSOCIOGRAMTST CONFORMITYYTY 14 WORDS ANSWERS.
General information CSE : Probabilistic Analysis of Computer Systems
Topic Models in Text Processing IR Group Meeting Presented by Qiaozhu Mei.
Institute for Statistics and Econometrics Economics Department Humboldt University of Berlin Spandauer Straße Berlin Germany CONNECTED TEACHING.
Chapter 14 Monte Carlo Simulation Introduction Find several parameters Parameter follow the specific probability distribution Generate parameter.
Optimal Allocation in the Multi-way Stratification Design for Business Surveys (*) Paolo Righi, Piero Demetrio Falorsi 
Machine Learning Lecture 23: Statistical Estimation with Sampling Iain Murray’s MLSS lecture on videolectures.net:
1 Lesson 8: Basic Monte Carlo integration We begin the 2 nd phase of our course: Study of general mathematics of MC We begin the 2 nd phase of our course:
Hit-and-Miss (or Rejection) Monte Carlo Method:
Monte Carlo Methods1 T Special Course In Information Science II Tomas Ukkonen
Mixture Models, Monte Carlo, Bayesian Updating and Dynamic Models Mike West Computing Science and Statistics, Vol. 24, pp , 1993.
Computation Model and Complexity Class. 2 An algorithmic process that uses the result of a random draw to make an approximated decision has the ability.
Hit-and-Miss (or Rejection) Monte Carlo Method: a “brute-force” method based on completely random sampling Then, how do we throw the stones and count them.
4. Numerical Integration. Standard Quadrature We can find numerical value of a definite integral by the definition: where points x i are uniformly spaced.
Crypto Final Presentation B 林敬倫 B 李佳蓉 B 王姵瑾 B 周振平.
Computer simulation Sep. 9, QUIZ 2 Determine whether the following experiments have discrete or continuous out comes A fair die is tossed and the.
Expectation-Maximization (EM) Algorithm & Monte Carlo Sampling for Inference and Approximation.
Write and Graph Equations of Circles Chapter 10: Circles.
CS774. Markov Random Field : Theory and Application Lecture 15 Kyomin Jung KAIST Oct
MAT 4830 Mathematical Modeling 04 Monte Carlo Integrations
CS Statistical Machine learning Lecture 25 Yuan (Alan) Qi Purdue CS Nov
Daphne Koller Overview Conditional Probability Queries Probabilistic Graphical Models Inference.
EMIS 7300 SYSTEMS ANALYSIS METHODS FALL 2005 Dr. John Lipp Copyright © 2005 Dr. John Lipp.
Monte Carlo Sampling to Inverse Problems Wojciech Dębski Inst. Geophys. Polish Acad. Sci. 1 st Orfeus workshop: Waveform inversion.
Stat 223 Introduction to the Theory of Statistics
Lesson 8: Basic Monte Carlo integration
Optimization of Monte Carlo Integration
ICS 280 Learning in Graphical Models
CSE 167 [Win 17], Lecture 11: Curves Problems Ravi Ramamoorthi
Approximate Inference Methods
Sampling Distributions for a Proportion
Path Coupling And Approximate Counting
Using Bayesian Networks to Predict Test Scores
Predictive distributions
Randomized Algorithms CS648
Bin Fu Department of Computer Science
Randomized Algorithms CS648
Monte Carlo rendering , , Computational Photography Fall 2018, Lecture 26
Stat 223 Introduction to the Theory of Statistics
Simple Sampling Sampling Methods Inference Probabilistic Graphical
Algorithms of POS Tagging
Topic Models in Text Processing
VQMC J. Planelles.
Homework: Maintenance Sheet 28 via Study Island- Answer all 30 questions in diagnostic test
Homework: Maintenance Sheet 28 via Study Island- Answer all 30 questions in diagnostic test
Find the area of the shaded sector.
Distribution-free Monte Carlo for population viability analysis
Introduction to Inference
Accuracy of Averages.
Presentation transcript:

Randomized Algorithms Randomized Algorithms CS648 Lecture 9 Random Sampling part-I (Approximating a parameter) Lecture 9 Random Sampling part-I (Approximating a parameter) 1

Overview of the Lecture Randomization Framework for estimation of a parameter 1.Number of balls from a bag 2.Size of transitive closure of a directed graph An Inspirational Problem from Continuous probability

AN INSPIRATIONAL PROBLEM FROM CONTINUOUS PROBABILITY

0 1

0 1 Sampling points on a line segment 0 1

Sampling points on a Circle (of circumference 1) 1

Transforming a line segment to a circle (just a different perspective) The knot formed by joining the ends of the line segment Give the knot a uniformly random rotation around the circle

Transforming a line segment to a circle (just a different perspective) First uniformly random point is the knot.

0 1 We have got the answer of the problem (without any knowledge of continuous probability theory) 0 1

ESTIMATING THE NUMBER OF BALLS IN A BAG

Estimating the number of Balls in a BAG 4 t n j q :c:c : i l l : : : :: :

4 t n j q :c:c : i l l : : : :: : Can we use it to design an algorithm ?

Estimating the number of Balls in a BAG 4 t n j q :c:c : i l l : : : :: :

How good is the estimate ? 2 N 1 N-1 multiple sampling.

Multiple samplings to improve accuracy and reduce error probability 21N

A better algorithm for estimating the number of balls:

21N

Final result

Randomized framework for estimating a parameter

ESTIMATING THE SIZE OF TRANSITIVE CLOSURE OF A DIRECTED GRAPH

Estimating size of Transitive Closure of a Directed Graph

Randomized Monte Carlo Algorithm for estimating the size of transitive closure of directed graph

MIN-Label Problem

Inference from the inspirational problem

RANDOMIZED MONTE CARLO ALGORITHM FOR ESTIMATING THE SIZE OF TRANSITIVE CLOSURE OF A DIRECTED GRAPH

Estimating size of Transitive Closure of a Directed Graph

0 1 Can you answer Question 2 now ?

Estimating size of Transitive Closure of a Directed Graph

Homework Use Chernoff bound to get a high probability bound on the error. Hint: Proceed along similar lines as in the case of estimating number of balls in a bag. Make sincere attempts to do this homework. I shall discuss the same briefly in the beginning of the next class.