## 基本素養 Basic Literacy

Ethical Reasoning
Graduate students should be able to identify ethical dilemmas and to determine necessary courses of action

Global Vision
Graduate students should possess a global statistical perspective and an awareness of the global business

## 核心能力 Competence

Speaking
Graduate students should be able to appreciate statistical research and to present research findings/ results effectively in speaking

Writing
Graduate students should be able to appreciate statistical research and to present research findings/ results effectively in writing

Interdiscip. Competence/ Prob. Solving
Graduate students should be able to integrate different functional areas in solving statistical problems

Critical Thinking/ Innovation
Graduate students should be able to analyze data effectively and to recommend effective statistical methods

Graduate students should be able to demonstrate leadership skills of a data analysis manager

Teamwork
Graduate students should be able to coordinate actions and solve problems jointly with other members of a professional team

## 課程概述 Course Description

This is an introductory course in stochastic processes, which introduces the most fundamental ideas in the area of modeling and analysis of real World phenomena in terms of stochastic processes. The course covers different classes of Markov processes: discrete and continuous-time Markov chains, Brownian motion and renewal processes etc.
This is an introductory course in stochastic processes, which introduces the most fundamental ideas in the area of modeling and analysis of real World phenomena in terms of stochastic processes. The course covers different classes of Markov processes: discrete and continuous-time Markov chains, Brownian motion and renewal processes etc.

## 課程學習目標 Course Objectives

• introduce to students with the idea of a random process
• help develop basic skills to understand logically the ideas behind theories
• introduce to students advanced topics in the theory of runs and patterns
• ## 課程進度 Progress Description

進度說明 Progress Description
1preliminaries
2the Poisson processes
3the Poisson processes
4renewal theory
5renewal theory
6renewal theory
7Markov chains: Introduction
8Markov chains: Introduction
9random walks
10Brownian motion: Introduction
11midterm exam
12runs and patterns
13finite Markov chain imbedding theory
14finite Markov chain imbedding theory
15applications of finite Markov chain imbedding
16applications of finite Markov chain imbedding
17final project
18final project
以上每週進度教師可依上課情況做適度調整。The schedule may be subject to change.

## 有關課程其他調查 Other Surveys of Courses

1.本課程是否規劃業界教師參與教學或演講? 否
Is there any industry specialist invited in this course? How many times? No
2.本課程是否規劃含校外實習(並非參訪)? 否
Are there any internships involved in the course? How many hours? No
3.本課程是否可歸認為學術倫理課程? 否
Is this course recognized as an academic ethics course? In the course how many hours are regarding academic ethics topics? No
4.本課程是否屬進入社區實踐課程? 否
Is this course recognized as a Community engagement and Service learning course? Which community will be engaged? No