NCHU Course Outline

Course Name	(中) 隨機數據分析(6765)
	(Eng.) Random Data Analysis
Offering Dept	Department of Civil Engineering
Course Type	Elective			Credits	3	Teacher	CHEN CHIA JENG
Department	Department of Civil Engineering / Graduate			Language	Chinese	Semester	2026-FALL
Course Description	(中) 涵蓋課題： 1. 馬可夫鍊； 2. 時間序列； 3. 赫斯特現象； 4. 狀態空間簡介。 (Eng.) Topics covered: 1. Markov chain; 2. Time series; 3. Hurst phenomenon; 4. Introduction to state-space representation.
Prerequisites

Relevance of Course Objectives and Core Learning Outcomes(%)			Teaching and Assessment Methods for Course Objectives
Course Objectives	Competency Indicators	Ratio(%)	Teaching Methods	Assessment Methods
Understand the essentials of stochastic processes	1.Capability in computation and analysis for civil engineering theory 2.Capability in analysis, evaluation, design, and implementation for civil engineering practices with contemporary tools 3.Capability in project management, communication, team work, and problem-solving 4.Acknowledgements in contemporary issues with international perspective, societal responsibilities, engineering and information ethics, and continuing learning as civil engineers 5.Professional knowledge in structural engineering, hydraulic engineering, geotechnical engineering, geomatics, construction management, hazard mitigation, and sustainable engineering 6.Capability in the initiation and fulfillment of research proposals and writing professional theses	30 30 10 10 10 10	Lecturing Discussion Exercises topic Discussion / Production	Quiz Assignment Oral Presentation Attendance Written Presentation
Course Content and Homework/Schedule/Tests Schedule
Week Course Content Week 1 1. Overview and review of probability and statistics; Week 2 1. Overview and review of probability and statistics; Week 3 2. Markov chain (Discrete-time Markov chain, state transition and probability, reducibility, periodicity, ergodicity, steady-state analysis, applications, etc.); Week 4 2. Markov chain (Discrete-time Markov chain, state transition and probability, reducibility, periodicity, ergodicity, steady-state analysis, applications, etc.); Week 5 2. Markov chain (Discrete-time Markov chain, state transition and probability, reducibility, periodicity, ergodicity, steady-state analysis, applications, etc.); Week 6 2. Markov chain (Discrete-time Markov chain, state transition and probability, reducibility, periodicity, ergodicity, steady-state analysis, applications, etc.); Week 7 Mini project I Week 8 3. Time series (AR, MA, ARMA, ARIMA, applications, etc.); Week 9 3. Time series (AR, MA, ARMA, ARIMA, applications, etc.); Week 10 3. Time series (AR, MA, ARMA, ARIMA, applications, etc.); Week 11 3. Time series (AR, MA, ARMA, ARIMA, applications, etc.); Week 12 Mini project II Week 13 4. Geostatistics (Kriging); Week 14 4. Geostatistics (Kriging); Week 15 4. Geostatistics (Kriging); Week 16 4. Geostatistics (Kriging);i. Other topics (e.g., state-space representation, optimal estimation, and Kalman filter); Final project self-directed learning
Evaluation
出席狀況(20%)、習作(30%)、期中報告(20%)、期末報告(30%)
Textbook & other References
1. Random Functions and Hydrology By Rafael L. Bras, Ignacio Rodríguez-Iturbe
Teaching Aids & Teacher's Website
iLearning
Office Hours
每週一早上11:00~12:00

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Update Date, year/month/day：None	Printed Date, year/month/day：2026 / 8 / 26
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