Definitions
Mixed Strategy Equilibrium
Iterated Strict Dominance
Rationalizability
Correlated equilibrium
Game Theory
Slide set 2: Static Games with Complete
Information
Lehigh University
Summer 2017
Game Theory
Lecture 2: Static Games with Complete Infor
Chapter 4: Random Variables and Discrete Distributions
4.0 Random Variables
The concepts of random variables and probability distributions are extremely important
in probability and statistics. Once you see clearly in your mind what they are, and how
they
IE 111 Fall Semester 2017
Homework #2 Solutions
Question 1
A manufacturing operation consists of 12 operations. However, seven machining
operations must be completed before any of the remaining five assembly operations can
begin. Within each type of opera
IE 111
Exam 1.1
Fall 2016
NAME_
Instructions
Open book, open notes. Calculator is allowed. No cell phones
Clearly indicate your answer. If I cant read it or figure it out, no points.
You must show all relevant work and justify your answers appropriatel
IE 111 Fall 2017
Homework #3 SOLUTIONS
1. Learn the concept of conditional probability by Venn diagram
a) If P(A|B) =1, must A=B? Draw a Venn diagram to explain your answer?
Answer:
From the Venn diagram, it satisfies P(A|B)=1, but AB.
b) Suppose A and B
ISE 111 Fall Semester 2017
Homework #4 Due Friday October 6
Question 1.
Suppose X has the following simple PMF
P(X=0) = 1/4
P(X=2) = 1/4
P(X=4) = 1/4
P(X=8) = 1/4
Suppose we define a new random variable Y as Y = X 2
a) Find the PMF of Y, PY(y)
b) Find Pr(
IE 111 Fall 2017
Homework #3 Due Friday 9/22/2017
1. Learn concepts of conditional probability by Venn diagram
a) If P(A|B) =1, must A=B? Draw a Venn diagram to explain your answer?
b) Suppose A and B are mutually exclusive events. Construct a Venn diagra
IE 111 Fall Semester 2017
Homework #1
Due Friday 9/8/17
Please turn in answers to all questions via the link provided on course site
Question 1
A local weather station has equipment designed to measure total snowfall in the Lehigh
Valley area this winter
HW 1 Solutions
1:
A local weather station has equipment designed to measure total snowfall in the
Lehigh Valley area this winter to the nearest inch.
a) What is the sample space for this experiment? Explain your choice.
Answer: All integers from 0 to infi
IE 111 Fall Semester 2017
Homework #2
Due Friday 9/15 at 9:00 am
Please turn in a file with answers to questions 1 to 7 and question 9. Turn in a separate
Excel file for question 8.
Question 1
A manufacturing operation consists of 12 operations. However,
IE 111 Fall Semester 2017
Chapter 1 Introduction to Probability
1.0 Introduction to the Course
This course will cover the fundamentals of probability theory. It is essentially a mathematics
course (indeed the Accreditation Board of Engineering and Technol
IE 111 Fall Semester 2017
Homework #1
Due Friday 9/8/17
Please turn in answers to all questions via the link provided on course site
Question 1
A local weather station has equipment designed to measure total snowfall in the Lehigh
Valley area this winter
Part I
Question 1 to 7 are finished by using Modeler stream file and
uploaded to Course Site.
Part II
a. (10 points) Briefly define the 3 new variables you created from
step 2 above and why you thought they may add value for your
modeling.
At the beginnin
I
3b
ii
iii
6b
6d
iv
6a
6c
V
vi
vii
7f
Viii
7g
ix
1
data audit node and filler
2
integral value. Because they would avoid string problems and prevent the matches will
occur.
3
6 oclock
Because before 6 oclock, the ray of light is not good enough, so the t
IE 365/465 Homework 2 Due March 10, 2017 in class
The purpose of this assignment is to predict the past 12 months per capita income of
Pennsylvania cities with population greater than 20,000 using a linear regression trained on all
data from other cities
ISE 465 Data Mining Homework 4 Due April 28, 2017 in Class
You work for a wine magazine as a wine reviewer and you would like to use analytics to
help you predict which wines will be of high quality. You have attributes of wines you have
rated in the past
ISE 365/465 Final Exam Review (Bold items will be
more heavily covered in the exam)
1. You should understand the SEMMA / CRISP-DM Modeling
framework.
2. You must know the function and settings that we covered in
class of the following IBM SPSS Modeler and
ISE 465 Data Mining Lab Data Merging and Exploration in IBM SPSS Modeler
Feb. 17, 2016 in class
Description: In this lab, you have two SAS files. One file is called CarAttributes and contains
information on the characteristics of the cars. The other file
Continuous Time Markov Chains
ISE 339
1
Continuous Time Markov Chains
A stochastic process cfw_X(t), t 0 is a continuous
time Markov chain (CTMC) if for all s, t 0 and
nonnegative integers i, j, x(u), 0 u < s,
P X s t j X s i, X u x u , 0 u s
P X s t j
Renewal Processes
IE 339
1
Renewal Processes
Poisson Process:
Counting process
iid exponential
times between
arrivals
Relax
counting
process
Continuous Time
Markov Chain:
Exponential times
between transitions
Relax
Renewal Process:
exponential
Counting pr
Exponential Distribution
and
Poisson Process
ISE 339
1
The Exponential Distribution
This is the most frequently used distribution for
modeling interarrival and service times in
queueing systems.
In many applications, it is regarded as doing a
good job o
Markov Chains Review
ISE 339
1
What is a Markov Chain?
Definition: A discrete-time stochastic process
is a Markov chain if, for t = 0,1,2 and all
states
P(Xt+1 = it+1 | Xt = it , Xt-1=it-1,X1=i1, X0=i0)
=P(Xt+1=it+1 | Xt = it)
Essentially this says that
IBM SPSS Modeler 18.0 User's Guide
IBM
Note
Before you use this information and the product it supports, read the information in Notices on page 205.
Product Information
This edition applies to version 18, release 0, modification 0 of IBM SPSS Modeler and
ISE 465 Applied Data Mining
Overview, Ch 1. and 4.1-2
Derya Pamukcu
Industrial and Systems Engineering Dept.
Lehigh University
Spring 2017
V 1.0
Based on Slides from Mike Magent, and the publisher's slides
1
Class Agenda
Class Roster
Course Coverage
Syll
Review from Last Lecture and
Todays Content
Last time we saw:
Basic stream building overview
Basic nodes like type, filter, derive, etc.
Data Cleaning techniques in Modeler
Data Audit Node
Graphs for Visualization
Graphboard and other Graph Nodes
Today
Class Agenda
Introduction to Modeler Stream Building
Source Nodes How to Read Data into Modeler
Type Node, Filter Node, Derive Node, Filler Node, Select Node,
Sort Node, Field Reorder Node, Table Node
Data Preprocessing
Data Summarization
Aggregation Nod
Class Agenda
Principal Components Analysis Example
using Linear Regression Example from Last
Class Lab
Model Evaluation
We saw Prediction Evaluation previously
Today we will see Classification Model
Evaluation
Model Comparison Node for Classification
Mo