Chapter 3 Probability
3.1 Probability:
Probability is a mathematical measure of the likelihood of an
event occurring. Probabilities are always fractions or decimals
indicating the portion or percent of the time that the event occurs.
Examples:
40% chance
1
Week 02: 2 and 4 September 2008
This week, we will review the fundamental theorem of the IVP
y = f (t, y)
y(0) = y0 ,
(1)
and begin to analyze and construct numerical methods.
2
Fundamental theorems of IVP theory
The fundamental theorems of the IVP guar
1
Week 05: 23 and 25 September 2008
We now begin the discussion of software issues such as step size control (Sects.
II.3 and II.4 of [HNW93], Chap. 7 of [Sha94], and Sect. 5.10 of [Lam91]),
continuous output ([HNW93] Sect. II.5) and automatic stiffness d
Understanding Electronics Components
author: Fili
This book is meant for those people who want to create electronic
All components are illustrated and the circuit-symbol is explained
complex examples are provided for the beginners. These include
transform
BASIC ELECTRONICS TEST
TEST GUIDE
WHY DO AT&T AND ITS AFFILIATES TEST?
At AT&T, we pride ourselves on matching the best jobs with the best people. To do this, we need to better
understand your skills and abilities to make sure that you are indeed the righ
M.G. University
EN010 109: Basic Electronics Engineering and Information Technology
(Common to all branches)
Teaching Scheme
Credits: 5
2 hour lecture and 1 hour tutorial per week
Objectives
To provide students of all branches of engineering with an over
Guide to Addressing Poor Performance
As stated in the Meeting Performance Standards policy, there will be specific steps
taken when employee performance problems arise. Using a consistent approach
across the entire company will help to avoid bias and/or h
NZQA registered unit standard
18240 version 7
Page 1 of 5
Title
Demonstrate knowledge of basic electronic components
Level
2
Purpose
Credits
5
This unit standard is intended for use in high school or preemployment electronics courses, or in the training o
EE 312 Basic Electronics Instrumentation Laboratory
Experiment 4 Bridges
(Formerly Called Impedance Measurements)
Fall 2000
Reference: Lecture 4 on http:/www.ee.buffalo.edu/~whalen/ee312
PROCEDURE:
0. Function Generator Settings
Make the following connect
Electronics and Robotics
(.5 credit)
Approved May 2011
1
Fundamentals of Electronic Technology and Safety Issues
Essential Understandings:
1. The resources, processes, concepts, and tools of technology must be used safely and effectively.
2. The study of
Achievement Objective:
Investigate situations that involve elements of chance:
Calculating probabilities of independent, combined and conditional
events
Exemplar 1
Graham and Samantha play a game of cards. They each have a set of cards numbered 1
to 10. T
The Application of Probability in the FluhrerMantin-Shamir Method of RC4 Key Recovery
Doug Madory
Term Project
ENGS 103
Winter Term 2005
Background
RC4 encryption is the encryption used in most software applications today (to include
HTTPS and SSL), which
1. Are there previous conclusive reports on this reaction?
+1
0
0
2. Did the adverse event occur after the suspected drug was
administered?
+2
-1
0
3. Did the adverse reaction improve when the drug was discontinued
or a specific antagonist was administere
Unit Plan Information
Project Title
Probability
Name/Grade
Debbie Kosiorek
Subject/Topics
Statistics
Establish learning objectives
Address content standards as you determine:
If students remembered one thing about this study, what would it be? Determining
A Review of Statistics and Probability for
Business Decision Making under Risk
Extracted from
http:/home.ubalt.edu/ntsbarsh/Business-stat/opre504.htm
http:/home.ubalt.edu/ntsbarsh/opre640a/partIX.htm
Statistical concepts and statistical thinking enable yo
The Finite Element Method Lecture Notes
Per-Olof Persson
[email protected]
April 23, 2013
1
1.1
Introduction to FEM
A simple example
Consider the model problem
u (x) = 1, for x (0, 1)
u(0) = u(1) = 0
(1.1)
(1.2)
with exact solution u(x) = x(1 x)/2. Fin
1
Week 04: 16 and 18 September 2008
We now begin the definition and construction of Runge-Kutta methods.
These onestep methods are essentially always stable, but designing Runge
Kutta methods which are consistent to high order can be difficult. This
theor
Iterative methods for linear systems
Chris H. Rycroft
November 20th, 2007
Introduction
For many elliptic PDE problems, nite-dierence and nite-element methods are the techniques of choice. In a nite-dierence approach, we search for a solution uk on a set o
UCB Math 228A, Fall 2014: Homework Set 2
Due September 29, 2014
1. The matlab script poisson.m solves the Poisson problem on a square
mm grid with x = y = h, using the 5-point Laplacian. It is set up to
solve a test problem for which the exact solution is
UCB Math 228A, Fall 2014: Homework Set 1
Due September 15, 2014
1. Read the paper, Eror Analysis of the Bjrck-Pereyra Algorithms for Solvo
ing Vandermonde Systems, by N. J. Higham, in Numerische Mathematik
vol. 50, pp. 613632, 1987. Implement a Matlab pro
1
Week 01: 28 August 2008
These notes will cover the numerical solution of initial value problems (IVPs)
and boundary value problems (BVPs) for ordinary dierential equations
(ODEs) and ordinary dierential-algebraic equations (DAEs). Well review
the basic
UCB Math 228A, Fall 2014: Homework Set 3
Due Oct. 20, 2014
1. The m-le iter bvp Asplit.m implements the Jacobi, Gauss-Seidel, and SOR matrix splitting methods on the linear system arising from the boundary value problem u (x) = f (x) in one space dimensio
UCB Math 228A, Fall 2014: Homework Set 4
Due Nov. 3, 2014
Code Submission: E-mail all requested and supporting MATLAB les to
Luming at [email protected] as a zip-le named lastname rstname 4.zip,
for example luming wang 4.zip.
1
2
3
4
UCB Math 228A, Fall 2014: Homework Set 5
Due Nov. 24, 2014
Code Submission: E-mail all requested and supporting MATLAB les to
Luming at [email protected] as a zip-le named lastname rstname 5.zip,
for example luming wang 5.zip.
1
2
3
4
1
Week 10: 28 and 30 October 2005
Next we will derive and analyze linear multistep and multivalue methods
for stiff and nonstiff problems. Multistep methods are covered in [HNW93]
Chap. III (nonstiff) and [HW96] Chap. V (stiff), as well as [Gea71] Chap. 7
Lecture Slides
Per-Olof Persson
IVP Theory and Basic Numerical Methods
[email protected]
Department of Mathematics
University of California, Berkeley
Math 228A Numerical Solutions of Differential Equations
Reduction to First Order and Autonomous System
Finite Difference Methods for PDEs
Per-Olof Persson
[email protected]
Department of Mathematics
University of California, Berkeley
Math 228A Numerical Solutions of Differential Equations
Finite Difference Methods for Elliptic Problems
Elliptic Partial
Finite Difference Methods for PDEs
Finite Difference Methods for Elliptic Problems
Per-Olof Persson
[email protected]
Department of Mathematics
University of California, Berkeley
Math 228A Numerical Solutions of Differential Equations
Elliptic Partial
Lecture Slides
Per-Olof Persson
[email protected]
Department of Mathematics
University of California, Berkeley
Math 228A Numerical Solutions of Differential Equations
IVP Theory and Basic Numerical Methods
Reduction to First Order and Autonomous System
1
Week 11: 4 and 6 November 2008
We now discuss the theory of fixedorder constantstepsize linear multistep
methods. This classical theory is presented in [HNW93] Sect. III.14 (nonstiff) and [HW96] Sect. V.1 (stiff), as well as [Gea71] Chap. 7, [Lam91] Cha