This site will launch

BIOS6170

Probability and Distribution Theory

10 Units

This unit involves the study of basic probability and calculus-based methods underpinning probability distributions, and parameter estimation. It begins with the concepts of probability, random variables, discrete and continuous distributions, and then discusses the use of calculus to obtain expressions for key parameters of these distributions, such as the mean and variance, and to investigate transformations of these distributions. Methods of estimation of these parameters based on a random sample from a probability distribution will then be presented. The central role of the normal distribution will be emphasised, together with transformations to normality and large sample properties of estimators. Numerical simulation will be used as a tool to demonstrate key concepts. This course is offered in conjunction with the Biostatistics Collaboration of Australia (BCA)

Faculty Faculty of Health and Medicine
School School of Medicine and Public Health
Availability Semester 2 - 2014 (Distance Education - Callaghan)
Objectives

On completion students should:

  1. Demonstrate an understanding of the meaning and laws of probability
  2. Apply calculus-based tools to derive key features of a probability distribution, such as mean and variance
  3. Understand the theoretical basis for estimation via likelihood based methods
  4. Apply calculus based tools to derive estimators from likelihood functions
  5. Understand properties of parameter estimators and the usefulness of large sample approximations in statistics
  6. Appreciate the role of simulation in demonstrating and explaining statistical concepts
Content

The teaching mode is off-campus. Written material together with selected textbook readings and biostatistical applications will introduce basic concepts. Detailed study of the theoretical derivation of basic probability and distribution theoretic results together with numerical simulation will provide a deeper understanding of important general principles (objectives 1,2,6). Statistical estimation will be introduced using first principles and properties demonstrated by theory and numerical simulation (objectives 3,4,5,6). A key component of students’ learning will be to attempt many of the suggested exercises (objectives 2,3,4,5). Structured practical exercises for assessment will give students the ability to provide theoretical justification for standard estimation procedures and prepare students to be able to apply general estimation principles in more complex settings (objective 3,4,5). Structured on-line exercises will provide students with an opportunity to demonstrate their understanding of key concepts, to receive feedback, and obtained responses to queries from unit coordinators and other students (objectives 1-6).

Replacing Course(s) NA
Transition N/A
Industrial Experience 0
Assumed Knowledge Mathematical Background for Biostatistics (BIOS6040);
Modes of Delivery Distance Learning : IT Based
Teaching Methods Email Discussion Group
Self Directed Learning
Assessment Items
  • Essays / Written Assignments - Two written assignments each worth 35% and submission of selected practical written exercises worth 30%
Contact Hours
  • Email Discussion Group: for 2 hour(s) per Week for Full Term
  • Self Directed Learning: for 8 hour(s) per Week for Full Term
Compulsory Components None listed
Course Materials None listed
Timetable 2014 Course Timetables for BIOS6170

Sound like the course for you?

  Apply Now