20 Points

This paper introduces the ideas behind and some methods for drawing a sample from the incompletely known (unscaled) posterior distribution. Parameter estimates are calculated from this sample. The development and computational implementation of these methods such as Markov chain Monte Carlo (MCMC) is a very important step in the field of statistics. It is behind the recent surge in the use of Bayesian methods in practical statistics, where inferences may now be drawn for very complicated models.

Co-ordinator(s)

• Dr. Bill Bolstad
  Room: G3.31
  Phone: +64 7 838 4466 ext 8334
  bolstad@waikato.ac.nz
  
  

Student Representatives
2011 Paul Brown paul_br0wn@hotmail.com

Lecturer
Dr Bill Bolstad

Timetable
The class will commence 20 July 2011. There will be 4 hours of lectures per week for the remaining weeks of the semester.

Lecture 1 Tuesday 2pm (A.G.12)

Lecture 2 Wednesday 3pm (G.3.33)

Lecture 3 Thursday 3-5pm (2 hours)(G.3.33)

Contact Hours
Three lectures and one tutorial each week.

Paper Handouts
Assignments: There will be approximately 7 Minitab projects to be handed in and marked. This will be the main internal assessment for the course. Other problems from the text will be assigned in class.

Pre Requisite Papers
STAT226 Bayesian Statistics or
STAT221 Statistical Data Analysis

Teaching Strategy
The course will cover chapters 1-11 of the text in order.

Attendance Policy
Class attendance is expected.

Expected Workload
This class will make extensive use of the statistical package Minitab on PC. This will be available in Computer Laboratory 5, R.G.12. Students should plan on spending about two to four hours per week in the Minitab laboratory.

Students should expect to spend a minimum of 10 hours per week on practical computational work and four hours per lecture, per week.

Important Dates
There will be one test in week 10 on Thursday 29 September 2011 from 3-5pm in G.3.33.

Assessment Schedule
The internal assessment for this course will consist of
Test - 30%
Minitab assignments - 70%.

Means of Assessment
The coursework : final exam ratio will be 1:1. However you must achieve at least 40% in both coursework and final exam to achieve an unrestricted pass.

Additional Notes
Course Notes: Copies of slides will be available on the Moodle webiste for this course. You can print any notes you require from the computer laboratory.

Handing in of Assignments: A labelled box will be placed opposite the Statistics office, G.2.18 (second floor, G Block), for all due assignments. Pleas ensure your name, student ID number and the assignment title is clearly labelled on your work.

Special consideration for missed work or impaired performance is covered in the University Calendar. Documentary evidence such as a medical certificate should be submitted directly to the Statistics Department Administrator, in Room G.2.18.

Academic Integrity
Follow this link for Academic Integrity information.

Student Concerns and Complaints
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