'X1 x2' diaporamas de présentation

2.1 Probability Joint & Conditional Probabilities Statistical Independence Random Variables & Distributions

2.1 Probability Joint & Conditional Probabilities Statistical Independence Random Variables & Distributions. Consider an Experiment with several Possible Outcomes, s i. Sample Space S = {s 1 , s 2 , s 3 ... s n } ? Set of All Possible Outcomes possibly infinite set .

1.56k views • 107 slides

Chapter 8 Inferences from Two Samples

Variation (one population) 6-4 Confidence interval for the population variance 7- 5 Inferences about the standard deviation 8-1 Inferences about Two Means: Independent and LARGE Samples 8-2 Inferences about Two Means: Matched Pairs 8-3 Comparing Variation in Two Samples

1.26k views • 87 slides

Dynamic-Programming Strategies for Analyzing Biomolecular Sequences

Dynamic-Programming Strategies for Analyzing Biomolecular Sequences. Kun-Mao Chao Department of Life Science National Yang-Ming University, Taiwan 2002 in Singapore E-mail: kmchao@ym.edu.tw WWW: http://www.ym.edu.tw/~kmchao. 1881 1991 2002 2112. Dynamic Programming.

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USING THE MATLAB COMMUNICATIONS TOOLBOX TO LOOK AT CYCLIC CODING

USING THE MATLAB COMMUNICATIONS TOOLBOX TO LOOK AT CYCLIC CODING. Wm. Hugh Blanton East Tennessee State University http://faculty.etsu.edu/blanton. The Theory. General Concepts. Channel Coding.

1.09k views • 56 slides

PSPACE and beyond

PSPACE and beyond. Aaron Bloomfield CS 4102 Spring 2011. PSPACE. Complexity classes. An aside: L and NL. L (a.k.a. LSPACE) is the set of algorithms that can be solved by a DTM in logarithmic space

1.01k views • 49 slides

MA110 Exam-AID Session

MA110 Exam-AID Session . By: Riley Furoy & Amanda Nichol. Agenda. Functions Symmetry Increasing/Decreasing Composite Inverse Exponent Laws Logarithmic Functions Laws of Logarithms Change of Base Formula The Natural Logarithm Limits Limit Laws Horizontal & Vertical Asymptotes

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Non-linear objectives in mechanism design

582 views • 46 slides

What Shape is your Method In?

What Shape is your Method In?. A Tutorial on the Application of Experimental Designs to Development of Chromatographic Methods By Dr Jeff Hughes School of Applied Science, RMIT University Melbourne , Australia.

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571 views • 41 slides

CPSC 335

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CS151 Complexity Theory

CS151 Complexity Theory. Lecture 7 April 23, 2013. Outline. 3 examples of the power of randomness communication complexity polynomial identity testing complexity of finding unique solutions randomized complexity classes. 1. Communication complexity. two parties: Alice and Bob

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Audio-Visual Graphical Models

Audio-Visual Graphical Models. Nebojsa Jojic Microsoft Research Redmond, Washington. Hagai Attias Microsoft Research Redmond, Washington. Matthew Beal Gatsby Unit University College London. Overview. Some background to the problem A simple video model A simple audio model

584 views • 29 slides

MANE 4240 & CIVL 4240 Introduction to Finite Elements

MANE 4240 & CIVL 4240 Introduction to Finite Elements. Prof. Suvranu De. Shape functions in 1D. Reading assignment: Lecture notes, Logan 2.2, 3.1. Summary: Linear shape functions in 1D Quadratic and higher order shape functions Approximation of strains and stresses in an element.

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Computational Methods for Management and Economics Carla Gomes

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Unit 4: Regression assumptions: Evaluating their tenability

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Physics 222 UCSD/225b UCSB

Physics 222 UCSD/225b UCSB. Lecture 1 Reminder: Bjorken Scaling QCD and Scaling violations Reminder: PDF Parton-Parton luminosities Outloook on this Quarter. Logic of what we did:. Electron - muon scattering in lab frame

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3.2.1. Augmenting path algorithm

3.2.1. Augmenting path algorithm. Two theorems to recall: Theorem 3.1.10 (Berge). A matching M in a graph G is a maximum matching in G iff G has no M- augmenting path.

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Approximations to Probability Distributions: Limit Theorems

Approximations to Probability Distributions: Limit Theorems. Sequences of Random Variables. Interested in behavior of functions of random variables such as means, variances, proportions For large samples, exact distributions can be difficult/impossible to obtain

480 views • 15 slides

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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Chapter 1

Chapter 1. Section 1. Examples:. 2 x 2 system. 2 x 3 system. 3 x 2 system. The order in which any two equations are written may be interchanged System 1: System 2: (x 1 ,x 2 ,x 3 ) is a solution to System 1 if any only if it is a solution to to System 2.

193 views • 8 slides