課程資訊
課程名稱
或然率理論與模型
Probability Theory and Modeling 
開課學期
100-1 
授課對象
工學院  水利工程組  
授課教師
蔡宛珊 
課號
CIE7165 
課程識別碼
521EM7540 
班次
 
學分
全/半年
半年 
必/選修
選修 
上課時間
星期四5,6,7(12:20~15:10) 
上課地點
土研402 
備註
本課程以英語授課。
總人數上限:30人 
Ceiba 課程網頁
http://ceiba.ntu.edu.tw/1001stochastic 
課程簡介影片
 
核心能力關聯
核心能力與課程規劃關聯圖
課程大綱
為確保您我的權利,請尊重智慧財產權及不得非法影印
課程概述

WHILE A SOPHISTICATED LEVEL OF QUANTITATIVE ABILITIES OR MATHEMATICAL MODELING IS NEEDED IN BOTH ACADEMIA AND PRACTICE, IT IS OFTEN PROMOTED THAT SUCH COURSES ARE ACADEMICALLY DIFFICULT SUBJECTS, RESERVED FOR THE TRULY GIFTED. WITH THE ADVANCEMENT OF TECHNOLOGY, STUDENTS SEEM TO RESORT TO COMPUTER SOFTWARE AS A PANACEA WITHOUT TRULY UNDERSTANDING THE BASICS OF THE PROBLEM. STUDENTS OFTENTIMES IGNORE THE FACT THAT MATHEMATICAL MODELING SHOULD NOT JUST BE PUNCHING NUMBERS INTO A
MODEL AND WAITING FOR WHAT COMES OUT FROM IT. LACK OF AN APPROPRIATE QUANTITATIVE SKILL COULD RESULT IN POOR DATA INTERPRETATION, INACCURATE MODELING AND IMPROPER ENGINEERING DESIGN. THE MAJOR OBJECTIVES OF THIS COURSE ARE TO STIMULATE STUDENTS’ LEARNING INTEREST IN PROBABILITY MODELING AND IMPROVE THEIR QUANTITATIVE SKILLS.
 

課程目標
THE OVERALL OBJECTIVE OF THIS COURSE IS TO FAMILIARIZE STUDENTS WITH FUNDAMENTAL AND EXTENDED CONCEPTS OF PROBABILITY THEORIES. STUDENTS ARE ANTICIPATED TO LEARN RELEVANT TOPICS SUCH A MARKOV CHAINS, POISSON PROCESSES, CONTINUOUS-TIME MARKOV PROCESSES, BROWNIAN MOTION AND SIMULATION TECHNIQUES. THIS COURSE WILL EQUIP STUDENTS WITH FUNDAMENTAL KNOWLEDGE AND QUANTITATIVE APPROACHES ESSENTIAL FOR PROBABILITY MODELING. 
課程要求
前置課程
STATISTICS
ENGINEERING MATHEMATICS I AND II OR EQUIVALENT 
預期每週課後學習時數
 
Office Hours
 
指定閱讀
 
參考書目
INTRODUCTION TO PROBABILITY MODELS” BY ROSS (9TH EDITION), ACADEMIC PRESS (2006)
"INTRODUCTION TO STOCHASTIC PROCESSES" BY HOEL ET AL. HOUGHTON MIFFLIN. (1972) 
評量方式
(僅供參考)
   
課程進度
週次
日期
單元主題
Week 1
9/15  Introduction 
Week 2
9/22  Uncertainty Analysis and Risk Assessment 
Week 3
9/29  Introduction to Point Estimate Methods 
Week 4
10/06  Parameter estimation 
Week 5
10/13  Introduction to Markov Chains 
Week 6
10/20  Discrete-time Markov Chains (1) 
Week 7
10/27  Discrete-time Markov Chains (2) 
Week 9
11/10  Continueous-time Markoc Chains (1) 
Week 10
11/17  Revisit to statistical distributions
&
Continueous-time Markov Chains (2) 
Week 11
11/24  The Exponenetial distribution and the Poisson process (1) 
Week 12
12/01  The Exponenetial distribution and the Poisson process (2) 
Week 13
12/08  Gambler's ruin problems 
Week 14
12/15  Birth and death processes (more examples) 
Week 15
12/22  A revised point estimate method 
Week 16
12/29  Dealing with non-detect data

& Introduction to R