Object-Oriented Analysis and
Design
CHAPTER 2: ITERATIVE, EVOLUTIONARY, AND AGILE
CHAPTER 3: CASE STUDIES
SLIDES: BY DR. SEMPER
1
What will we learn?
We will learn what iterative development is, and why it works
We will learn what Agile Modeling is, and h
Object-Oriented Analysis and
Design
C HAPT ERS 30, 7 : R EL AT ING US E CASES, OT HER REQ UIRE MENT S
SLIDES: BY DR. SEMPER
1
What will we learn?
How to relate use cases to each other
Other requirements, and how to capture them in artifacts
2
Relating Use
Object-Oriented Analysis and
Design
CHAPTERS 4-6: INCEPTION, REQUIREMENTS, USE CASES
SLIDES: BY DR. SEMPER
1
What will we learn?
Inception what is it?
How to analyze requirements in iterative development
The FURPS+ model, and the UP Requirements artifacts
Object-Oriented Analysis and
Design
CHAPTERS 8, 9: BASICS, INTRO TO DOMAIN MODELS
1
What will we learn?
The basics of OOA/OOD some key factors that are important in
Inception and Elaboration
The Domain Model what it is, how to start to build one from
scra
Object-Oriented Analysis and
Design
CHAPTER 1: INTRODUCTION
SLIDES: BY DR. SEMPER
1
What will we learn?
We will learn the skills needed for good object-oriented analysis and design
We will utilize Unified Modeling Language (UML)
Be careful: Just knowing h
Chapter 26
Applying Gang of Four Design
Patterns
CS6359 Fall 2012 John Cole
1
The Gang of Four
The book was Design Patterns by Gamma,
Helm, Johnson, and Vlissades
CS6359 Fall 2012 John Cole
2
Pattern Overload
As of 2000, there were over 500 published
de
Object-Oriented Analysis and
Design
CHAPTER 10: SYSTEM SEQUENCE DIAGRAMS
1
What will we learn?
System Sequence Diagrams What are they, how to create them
2
What are System Sequence
Diagrams?
Part of the UP Use-Case Model artifact, although usually created
Object-Oriented Analysis and
Design
CHAPTER 6: USE CASES, PART II
SLIDES: BY DR. SEMPER
1
What will we learn?
How to define use cases, how to find them, how to construct them
Applying UML use case diagrams
How to work with use cases in iterative methods
R
GRASP: Designing
Objects with
Responsibilities
Applying UML and Patterns
Craig Larman
Chapter 17
Glenn D. Blank CSE432, Lehigh
University
Chapter Learning Objectives
Learn about design patterns
Learn how to apply five GRASP patterns
Youve learned about s
Object-Oriented Analysis and
Design
CHAPTER 11, 32: OPERATION CONTRACTS & SYSTEM
SEQUENCE DIAGRAMS
1
What will we learn?
Operation Contracts how to define the system operations, and
how to create contracts for them
Define SSDs and Operation Contracts for
5.qxd
1/7/10
9:45 AM
Page 28
IES 302: Engineering Statistics
HAPTER 2 PROBABILITY
2011/2
HW Solution 1 Due: February 1
Lecturer: Prapun Suksompong, Ph.D.
2-18. In a magnetic storage device, three attempts are made
ES FOR SECTION 2-1
to read data before an
Section 2.2 Axioms and rules of probability
Here and elsewhere we shall not obtain the best insight into things until we actually see
them growing from the beginning
Aristotle, from the work Politics
Axioms of probability
A mathematically rigorous study o
Section 4.2(a) Families of Continuous Distributions
We balance probabilities and choose the most likely. It is the scientific use of the
imagination.
Sherlock Holmes, The Hound of the Baskervilles
Here we review some commonly used continuous distributions
CIS 2033 based on
Dekking et al. A Modern Introduction to Probability and Statistics. 2007
Slides by Michael Maurizi
Instructor Longin Jan Latecki
C9: Joint Distributions and Independence
9.1 Joint Distributions of Discrete
Random Variables
Joint Distribu
Complement
If A is an event, then the complement of A, written Ac, means
all the possible outcomes that are not in A.
For example, if A is the event UNC wins at least 5 football
games, then Ac is the event UNC wins less than 5 football
games.
1
We can rep
Sections 3.1 - 3.3 Random Variables
Expected Value of a Random Variable
This is the same formula as in physics for the center of mass (or of gravity) of a system
of point masses ( ) located at the points .
Example: If a coin is tossed 3 times, what is the
Section 2.1 Events as sets
The whole of science is nothing more than a refinement of everyday thinking.
Albert Einstein, from the essay Physics and Reality
Three interpretations of probability (and statistics)
I.
We have an intuitive idea of probability a
Ch. 17 Basic Statistical Models
CIS 2033: Computational Probability and Statistics
Prof. Longin Jan Latecki
Prepared by: Nouf Albarakat
Basic Statstcal Models
Random samples
Statstcal models
Distributon features and sample statstcs
Estmatng features of
Section 2.4 Conditional Probability
Section 2.4: Conditional Probability
If people do not believe that mathematics is simple, it is only because they do not
realize how complicated life is.
John Louis von Neumann
The conditional probability of event give
CIS 2033
Based on text book:
F.M. Dekking, C. Kraaikamp, H.P.Lopulaa, L.E.Meester. A
Modern Introduction to Probability and Statistics
Understanding Why and How
Instructor: Dr. Longin Jan Latecki
1
Chapter 15 Exploratory data analysis:
graphical summaries
CIS 2033 based on
Dekking et al. A Modern Introduction to Probability and Statistics. 2007
Michael Baron. Probability and Statistics for Computer Scientists, CRC 2006
Instructor Longin Jan Latecki
Ch. 6 Simulations
What is a simulation?
One uses a model t
Section 3.4 Families of Discrete Distributions
All models are wrong, but some are useful.
George Box, statistician
Some probability distributions occur frequently enough that we can study the
distribution itself and later use it to model a situation.
Here
Nave Bayes Classifier
Ke Chen
http:/intranet.cs.man.ac.uk/mlo/comp20411
/
Modified and extended by Longin Jan Latecki
latecki@temple.edu
Probability Basics
Prior, conditional and joint probability
P(X )
Prior probability:
Conditional probability:
P(X1| X
CIS 2033 based on
Dekking et al. A Modern Introduction to Probability and Statistics. 2007
Instructor Longin Jan Latecki
Chapter 5: Continuous Random Variables
Probability Density Function of X
A random variable (RV) X is continuous if there exists functi
Section 2.2 Axioms and rules of probability
Here and elsewhere we shall not obtain the best insight into things until we actually see
them growing from the beginning
Aristotle, from the work Politics
Axioms of probability
A mathematically rigorous study o
Lecture 11: The Bernoulli and Binomial Distributions
1. Definitions
Definition: A random variable X is said to be a Bernoulli random variable with parameter p if it takes values in the set cfw_0, 1 with probability mass function
p(0) = P(X = 0) = 1 p
p(1)
Introduction to Probability
Example Sheet 1 - Michaelmas 2006
Michael Tehranchi
Problem 1. Show that if F is a sigma-field on a set then that both and are elements
of F.
Solution 1. If A is in F, so is the complement Ac . Hence the union A Ac = is in F, a
Binomial Distribution
And general
discrete probability distributions.
Random Variable
A random variable assigns a number to a
chance outcome or chance event.
The definition of the random variable is
denoted by uppercase letters at the end of
alphabet, s