111年第1學期-1591 隨機過程 課程資訊

評分方式

評分項目	配分比例	說明
Assignments	30	2-3 assignments
Mid-term Exam	30
Project Report	40

教育目標

The objective of this course is to introduce basic concepts for stochastic processes. The main focus will be on studying analytical models for systems which change states stochastically with time. The topics include: 1. 隨機過程介紹 1A. 隨機過程與程式語言：Python程式語言運用於隨機過程 2. Markov Chains 2A. Applications of Markov Chains 3. Poisson processes 3A. Applications of Poisson processes 4. Continuous-Time Markov Chains 4A. Applications of Continuous-Time Markov Chains 5. Brownian Motion & Stochastic Integral 6. Linear State Space Model

課程概述

The objective of this course is to introduce basic concepts for stochastic processes. The main focus will be on studying analytical models for systems which change states stochastically with time. This is a course for studying probabilistic models rather than statistical models. Thus, background on probability and mathematical statistics are necessary. We will begin right after �onditional probability�and �onditional expectation� Students will learn concepts and techniques for characterizing models stochastically. This will help students for further study. The topics of this course include basic processes, stochastic models, and diffusion processes. Contents of this course might be adjusted according to time limitation and students�interests. They are: 1.Preliminaries: lack of memory property, transformations, inequalities, limit theorems, notations of stochastic processes 2.Markov chains: Chapman-Kolmogorov equation, classification of chains, long run behavior of Markov chains, branch processes, random walk 3.Poisson processes: Inter-arrival time distributions, conditional waiting time distributions, non-homogeneous Poisson processes 4.Continuous-time Markov chains: birth-death processes, compound Poisson processes, finite-state Markov chains 5.Renewal processes: renewal functions, limit theorems, delayed and stationary renewal processes, queueing 6.Stochastic models: Markov renewal processes, marked processes 7.Martingales: conditional expectations, filtrations, stopping time, martingale CLT 8.Diffusion Processes: Brownian motions, It�s formula, Black-Scholes Model, Girsanov Theorem

基本資料

選修課，學分數：3-0
上課時間：二/4[M108] 三/3,4[M135]
修課班級：統計系2-4
修課年級：年級以上
選課備註：需搭配「隨機過程實作」課程(時間為二/3)一同修習。工業統計群組(107-111適用)。深碗型課程，曾修習機率論

參考書目

1. Sheldon M. Ross (2019) Introduction to Probability Models, 12th ed, Academic Press
2. T. Nakagawa (2011) Stochastic Processes with Applications to Reliability Theory, Springer-Verlag
3. R. M. Feldman, C. Valdez-Flores (1996) Applied Probability & Stochastic Processes, PWS Publishing Co.