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 620-201 Probability

Note

Students may only gain credit for one of [99]620-001, [99]620-005, 620-201, 620-370, 431-325. Passing 620-201 precludes subsequent credit for 620-131, 620-152, 620-160.

Credit Points

12.5

HECS Band

2

Coordinator

Professor T Brown

Prerequisites

One of 620-111, 620-120 (MUPHAS Mathematics), 620-121, 620-141 and one of 620-131, 620-152, 620-160, 620-112, 620-122, [99]620-142, [99]620-200, 620-211, 620-113, 620-123, 620-143, [98]620-130, [98]620-132.

Semester

1 (view timetable)

Contact

36 lectures (three per week) and 22 tutorial/practice class hours (two per week)

Subject Description

This subject introduces the fundamental concepts of probability, probability modelling and simulation. Students should develop the ability to use simple probability models and carry out standard probability calculations. They should learn to apply standard distributions and to simulate to approximate probabilities. This subject demonstrates the breadth of application of probability, the principles of probability modelling and the application of computer software in probability calculations.

Simple probability models: Bernoulli trials, Poisson processes, sampling models, use of conditional probability. Random variables: descriptions of their probability distributions, probability mass function, probability density function, cumulative distribution function and quantiles, expectation. Standard distributions including hypergeometric, binomial, negative binomial, Poisson, normal, exponential and gamma distributions and their applications; mean, variance and other moments. Transformations: distribution of g(X), approximations to the mean and variance of g(X). Bivariate random variables: descriptions of their probability distributions; expectation of g(X,Y); bivariate normal distribution; covariance and correlation; independence of random variables; distribution of g(X,Y), where X and Y are independent, conditional distributions, conditional means and variances and their uses. Probability generating functions and applications; random sums; branching processes; Markov chains; simulation of random variables and processes. Moment generating functions and the central limit theorem.

Assessment

Up to 3 hours end-of-semester written examination; up to 50 pages of assignments may be assessed.



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