BIOSTATS 6014EX - Bayesian Statistical Methods

External - Semester 2 - 2018

The aim of this course is to achieve an understanding of the logic of Bayesian statistical inference, i.e. the use of probability models to quantify uncertainty in statistical conclusions, and acquire skills to perform practical Bayesian analysis relating to health research problems. Topics will include simple one-parameter models with conjugate prior distributions; standard models containing two or more parameters, including specifics for the normal location-scale model; the role of non-informative prior distributions; the relationship between Bayesian methods and standard 'classical' approaches to statistics, especially those based on likelihood methods; computational techniques for use in Bayesian analysis, especially the use of simulation from posterior distributions, with emphasis on the WinBUGS package as a practical tool; and application of Bayesian methods for fitting hierarchical models to complex data 最新糖心Vlog.

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Course Details

Course Code	BIOSTATS 6014EX
Course	Bayesian Statistical Methods
Coordinating Unit	Public Health
Term	Semester 2
Level	Postgraduate Coursework
Location/s	External
Units	3
Contact	Online
Available for Study Abroad and Exchange	N
Prerequisites	BIOSTATS 6000EX, BIOSTATS 6001EX, BIOSTATS 6003EX, BIOSTATS 6005EX, BIOSTATS 6006EX & BIOSTATS 6007EX
Restrictions	Available to Grad Cert, Grad Dip, M Biostatistics students only
Biennial Course	Course offered in even years
Assessment	Two written assignments 30% each, submission of selected practical exercises 40%

Course Staff

Course Coordinator: Dr Amy Salter

Course Coordinator : Dr Amy Salter
Phone: +61 8313 4619
Email: amy.salter@adelaide.edu.au
Location: Level 9, Adelaide Health and Medical Sciences building, North Terrace

Course Timetable

The full timetable of all activities for this course can be accessed from .

Learning Outcomes

Course Learning Outcomes

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Learning Resources

Required Resources

Recommended Resources

Online Learning
Learning & Teaching Activities

Learning & Teaching Modes

Workload

The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

Learning Activities Summary

Specific Course Requirements

Small Group Discovery Experience

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Assessment Summary

Assessment Related Requirements

Assessment Detail

Submission

Course Grading

Grades for your performance in this course will be awarded in accordance with the following scheme:

M10 (Coursework Mark Scheme)
Grade	Mark	Description
FNS		Fail No Submission
F	1-49	Fail
P	50-64	Pass
C	65-74	Credit
D	75-84	Distinction
HD	85-100	High Distinction
CN		Continuing
NFE		No Formal Examination
RP		Result Pending

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