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IV.4 Signal-to-Noise Ratios

IV.4 Signal-to-Noise Ratios

IV.4 Signal-to-Noise Ratios. Background Example. Background Motivation. Wouldn’t it be Nice to Have a Single Performance Measure that Simultaneously Identified Factor Settings that Optimally target the mean Reduce variation

By patsy
(160 views)

Completion Time Scheduling

Completion Time Scheduling

Completion Time Scheduling. Notes from Hall, Schulz, Shmoys and Wein, Mathematics of Operations Research, Vol 22, 513-544, 1997. One LP formulation for 1||Σw j C j. Other ways to bound C j. Smith’s rule: Scheduling jobs by w j /p j is guaranteed to be optimal. w j. p j.

By paley
(124 views)

Using Excel Solver for Linear Optimization Problems

Using Excel Solver for Linear Optimization Problems

Using Excel Solver for Linear Optimization Problems. Wendy Pitchko Irene Meglis Shawn Lemko. What is Solver?. Solver is an Add-In for Microsoft Excel which can solve optimization problems, including multiple constraint problems. You can maximize, minimize, or set a target value to achieve.

By kiril
(239 views)

Mini-course on algorithmic aspects of stochastic games and related models

Mini-course on algorithmic aspects of stochastic games and related models

Mini-course on algorithmic aspects of stochastic games and related models. Marcin Jurdziński (University of Warwick) Peter Bro Miltersen (Aarhus University) Uri Zwick ( 武熠 ) (Tel Aviv University). Oct. 31 – Nov. 2, 2011. Day 1 Monday, October 31.

By brie
(80 views)

Adaptive Playout Algorithm For VoIP

Adaptive Playout Algorithm For VoIP

Adaptive Playout Algorithm For VoIP. Voice Over IP (VoIP) is a common technology for performing voice calls over the Internet . While the Internet isn’t designed for real-time data transfer, packets of information will arrive to their destination with “jittering”.

By zuzela
(122 views)

Weighted log-partition function

Weighted log-partition function

Bounding the Partition Function Using Hölder’s Inequality. Qiang Liu Alexander Ihler Department of Computer Science, University of California, Irvine. Duality results. Graphical models. H ö lder’s inequality. Markov random fields Factorized form

By yates
(192 views)

Support Vector Regression

Support Vector Regression

Support Vector Regression. Artur Akbarov. Paper. A tutorial on support vector regression By Smola, A.J and Schölkopf , B. Statistics and Computing, 14, pp . 199-222 , 200 4. Why SVM?.

By dmitri
(1237 views)

Destriping VIIRS visible bands (preliminary results)

Destriping VIIRS visible bands (preliminary results)

Destriping VIIRS visible bands (preliminary results). Karlis Mikelsons Ocean Color Group Meeting May 30, 2014. Different kinds of striping. Minor d ifferences in detector calibration and sensitivity Small amplitude No direct relation with detector number Affects all bands

By duane
(108 views)

DUALITY

DUALITY

DUALITY. Duality. Duality is the relationship between the primal and its dual, both on a mathematical and economic level, that is truly the essence of duality theory.

By aziza
(423 views)

The Quality Improvement Model

The Quality Improvement Model

The Quality Improvement Model. Define Process. Select Measures. Collect & Interpret Data. Is Process Capable?. Is Process Stable ?. Investigate & Fix Special Causes. No. Purpose: Determine the adequacy of the process with respect to customer /management needs. Yes. Is Process Capable ?.

By marie
(133 views)

NLP Sensitivity Analysis

NLP Sensitivity Analysis

NLP Sensitivity Analysis. Sensitivity Analysis. LP Term NLP Term Meaning Shadow Price Lagrange Multiplier Marginal value of resources. Reduced Cost Reduced Gradient Impact on objective of small changes in optimal values of decision variables.

By korene
(167 views)

Maximization without Calculus

Maximization without Calculus

Maximization without Calculus. Not all economic maximization problems can be solved using calculus If a manager does not know the profit function, but can approximate parts of it by straight lines. . d  /d q does not exist at q *. *.  = f ( q ). Quantity. q*.

By jesus
(147 views)

Utility Maximization

Utility Maximization

Utility Maximization. Continued July 5, 2005. Graphical Understanding. Normal Indifference Curves. Downward Slope with bend toward origin. Graphical. Non-normal Indifference Curves. Y & X Perfect Substitutes. Graphical. Non-normal. Only X Yields Utility. X & & are perfect

By amal
(214 views)

Samuel Labi and Fred Moavenzadeh Massachusetts Institute of Technology

Samuel Labi and Fred Moavenzadeh Massachusetts Institute of Technology

Lecture 12 Resource Allocation Part II (involving Continuous Variable (Linear Programming, continued). 1.040/1.401/ESD.018 Project Management. Project Mgmt Project Mgmt Project Mgmt Project Mgmt Project Mgmt Project Mgmt Project Mgmt Project Mgmt Project Mgmt. Samuel Labi and Fred Moavenzadeh

By biana
(239 views)

Analysis and Selection of Myriad Estimate Tuning Parameter For S α S Distributions

Analysis and Selection of Myriad Estimate Tuning Parameter For S α S Distributions

Analysis and Selection of Myriad Estimate Tuning Parameter For S α S Distributions. Roenko A.A., Lukin V.V., Djurovi ć I. 1. Symmetric α - stable distributions and their properties. Characteristic function of random variable Х with S α S distribution :. (1).

By marlo
(115 views)

91.420/543: Artificial Intelligence UMass Lowell CS – Fall 2010

91.420/543: Artificial Intelligence UMass Lowell CS – Fall 2010

91.420/543: Artificial Intelligence UMass Lowell CS – Fall 2010. Lecture 17 & 18: Markov Decision Processes Oct 12–13, 2010. A subset of Lecture 9 slides from Dan Klein – UC Berkeley Many slides over the course adapted from either Stuart Russell or Andrew Moore. Reinforcement Learning.

By chelsi
(133 views)

Reinforcement Learning

Reinforcement Learning

Reinforcement Learning. Basic idea: Receive feedback in the form of rewards Agent’s utility is defined by the reward function Must learn to act so as to maximize expected rewards. This slide deck courtesy of Dan Klein at UC Berkeley. Grid World. The agent lives in a grid

By sumana
(539 views)

CSE 473: Artificial Intelligence

CSE 473: Artificial Intelligence

CSE 473: Artificial Intelligence. Markov Decision Processes (MDPs) Luke Zettlemoyer. Many slides over the course adapted from Dan Klein, Stuart Russell or Andrew Moore. 1. Announcements. PS2 online now Due on Wed. Autograder runs tonight and tomorrow. Lydia / Luke office hour:

By marnie
(104 views)

Lirong Xia

Lirong Xia

Markov Decision Processes. Lirong Xia. Tue, March 4, 2014. Reminder. Midterm Mar 7 in-class open book and lecture notes simple calculators are allowed cannot use smartphone/laptops/ wifi practice exams and solutions (check piazza) OH tomorrow (Lirong); Thursday ( Hongzhao ).

By arnav
(178 views)

CS 5368: Artificial Intelligence Fall 2010

CS 5368: Artificial Intelligence Fall 2010

CS 5368: Artificial Intelligence Fall 2010. Lecture 11: MDP + RL (Part1) 10/5/2010. Mohan Sridharan Slides adapted from Dan Klein. Reinforcement Learning. Basic idea: Receive feedback in the form of rewards. Agent’s utility is defined by the reward function.

By leona
(108 views)

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