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README.md

Raazesh Sainudiin - lamaste.org - Courses - Monte Carlo Methods

STAT 221: Monte Carlo Methods

Jenny Harlow, Raazesh Sainudiin and Dominic Lee,

Laboratory for Mathematical Statistical Experiments, Christchurch Centre Christchurch, New Zealand

© 2007–2016 Raazesh Sainudiin, © 2008–2013 Dominic Lee, © 2009-2011 Jennifer Harlow.

This work is licensed under the Creative Commons Attribution-Noncommercial-Share Alike 4.0 International License. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/.

Read about this course first!

Time and Place for Presence

  1. Three Weekly Face-to-Face Lectures in Erskine 4xx:

    1. Optional: download and view the videos of 2011 lectures from the YouTube PlayList: Monte Carlo Methods in Sage and
  2. Two Weekly Laboratory/Tutorial:

    1. On your own time in Erskine 4xx.

Course Syllabus and Expectations

  1. Note that you are expected to spend 6 to 8 hours per week on this course.

  2. Evaluation:

    • 16%: Assessment 1 on YYYYMMDD (electronically due at 2359 hours YYYYMMDD)
    • 16%: Assessment 2 has two parts with equal weights:
      1. Paper and Pencil Part: Assessment 2a (due at 1000 hours on YYYYMMDD)
      2. Sage Worksheet Part: published on YYYYMMDD (electronically due at 2359 hours YYYYMMDD)
    • 16%: Assessment 3 has two parts with equal weights:
      1. Paper and Pencil Part: Assessment 3a (due at 1000 hours on YYYYMMDD)
      2. Sage Worksheet Part: published on YYYYMMDD (electronically due at 2359 hours YYYYMMDD)
    • 12%: Weekly Tutorial Attendance and Course Participation
    • 40%: Final Exam (3 hours long, open book, open notes and open web)

    Lab Assessments should be electronically submitted. You need to submit your assignment as an sws file. Read How to download worksheet as sws file for electronic submission.

  3. See the weekly outline below for course material.

Course Outline

  1. Week 01:

  2. Week 02:

  3. Week 03:

    • STAT221Week03: Conditional Probability, Random Variables, Expectations, Loops and Conditionals (published at sage.ac.nz or sagenb.org or sagenb.com)
  4. Week 04:

  5. Week 05:

  6. Week 06:

  7. Week 07:

    • STAT221Week07: Pseudo-Random Numbers, Simulating from Some Discrete and Continuous Random Variables (published at sage.ac.nz or sagenb.org or sagenb.com)
  8. Week 08:

  9. Week 09:

  10. Week 10:

  11. Week 11:

  12. Week 12:

    • Lectures: Permutation Test and Monte Carlo Integration. Face-To-Face-Transmission - gotto be physically here for the last week!

Sage Work Sheets as SWS and PDF files

  1. STAT221Week01.sws and STAT221Week01.pdf
  2. STAT221Week02.sws and STAT221Week02.pdf
  3. STAT221Week03.sws and STAT221Week03.pdf
  4. STAT221Week04.sws and STAT221Week04.pdf
  5. STAT221Week05.sws and STAT221Week05.pdf
  6. STAT221Week06.sws and STAT221Week06.pdf
  7. STAT221Week07.sws and STAT221Week07.pdf
  8. STAT221Week08.sws and STAT221Week08.pdf
  9. STAT221Week09.sws and STAT221Week09.pdf
  10. STAT221Week10.sws and STAT221Week10.pdf
  11. STAT221Week11.sws and STAT221Week11.pdf