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Applied Stochastic process (Ⅱ)

Offered in 112-2

  • Serial Number

    68812

  • Course Number

    EPM8091

  • Course Identifier

    849 D0370

  • No Class

  • 2 Credits

  • Elective

    Graduate Institute of Epidemiology and Preventive Medicine / Health Data Analytics and Statistics / NATIONAL TAIWAN UNIVERSITY DISTANCE LEARNING / Master Program in Statistics of National Taiwan University / BIOLOGICAL STATISTICS

      Elective

    • Graduate Institute of Epidemiology and Preventive Medicine

    • Health Data Analytics and Statistics

    • NATIONAL TAIWAN UNIVERSITY DISTANCE LEARNING

    • Master Program in Statistics of National Taiwan University

    • BIOLOGICAL STATISTICS

  • HSIU-HSI CHEN
  • Wed 10, A

  • PUBLIC HEALTH BLDG. ROOM 213 (公衛213)

  • Type 2

  • 45 Student Quota

    NTU 45

  • No Specialization Program

  • English
  • NTU COOL
  • Core Capabilities and Curriculum Planning
  • Notes
    The course is conducted in English。

  • NTU Enrollment Status

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  • Course Description
    This course is an advanced stochastic process, which is the extension of basic stochastic process. The advances theory and complex stochastic process would be given and illustrated with practical examples.
  • Course Objective
    The objective of this advanced course is to delve deeper into recondite stochastic processes, equipping students to apply complex stochastic methods and address challenges in multi-state outcome research beyond the basics of stochastic processes.
  • Course Requirement
    Basic statistics
  • Expected weekly study hours after class
  • Office Hour

    If you have any question, please mail to TA (楊長融, r11849034@ntu.edu.tw 或 jeff88.yang@gmail.com)

    *This office hour requires an appointment
  • Designated Reading
  • References
    Cox, D.R., and Miller, H.D. (1965), The Theory of Stochastic Processes, London: Methuen
  • Grading
    40%

    Online video watching, Quiz, and homework

    30%

    Midterm exam (In person)

    30%

    Final exam (In person)

  • Adjustment methods for students
  • Course Schedule
    2/21Week 1Introduction to Random Walk Model
    2/28Week 2Advanced Random Walk Model
    3/06Week 3Review of Statistical Methods for Precision Medicine Approach
    3/13Week 4Random Walk Model with Type of Barrier
    3/20Week 5Review of Applied Stochastic Process (I)
    3/27Week 6Diffusion Processes
    4/03Week 7Semi-Markov Model (I)
    4/10Week 8Semi-Markov Model (II)
    4/17Week 9Midterm Exam (In person)
    4/24Week 10Application of Semi-Markov Model (I)
    5/01Week 11Application of Semi-Markov Model (II)
    5/08Week 12Hidden Markov Model (I)
    5/15Week 13Hidden Markov Model (II)
    5/22Week 14Bayesian Hidden Markov Model (I)
    5/29Week 15Bayesian Hidden Markov Model (II)
    6/5Week 16Final Exam (In person)