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Introduction

The unit develops foundation skills for the analysis of real-life systems with elements of uncertainty, useful for careers in Statistics, Physical and Biological Sciences, Operations Research, Engineering, Computer Science, Finance and Economics. The unit covers major topics from Probability Theory, with the focus on developing in-depth knowledge from both theoretical and modelling points of view.

Topics: Axiomatic probability theory: sample space, event, probabilities on events, independent events, Bayes' formula; Random variable, probability distribution, expectation, conditional probability; Distribution functions: discrete, continuous; joint distribution; probability generating function; Laplace transform; moment generating function.  Stochastic Processes: Bernoulli process; Poisson process; discrete-time Markov Chains: Chapman-Kolmogorov equations, classification of states, recurrence, limiting probabilities; continuous-time Markov Chains: Kolmogorov differential equations, embedded chains, equilibrium distributions.

Summary 2020

Unit name Probability Models 3
Unit code KMA305
Credit points 12.5
Faculty/School College of Sciences and Engineering
School of Natural Sciences
Discipline Mathematics
Coordinator

Teaching staff

Malgorzata O'Reilly

Level Advanced
Available as student elective? Yes
Breadth Unit? No

Availability

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About Census Dates

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Requisites

Prerequisites

  • Any Intermediate unit in Mathematics

Co-requisites

Mutual Exclusions

You cannot enrol in this unit as well as the following:

Teaching

Teaching Pattern

3 x 1-hr lectures, 1 x 1-hr tutorial , 1 x 1-hr lab weekly

Assessment

3hr exam (70%), assignments (30%)

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Textbooks

Required

Information about any textbook requirements will be available from mid November.

Recommended

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