60 hours

Hawthorn

Credit Points: 12.5 Credit Points

Related Course/s:

Aims & Objectives:

• Convey the fundamental probability concepts necessary for an understanding of stochastic process.
• Introduce stochastic processes and some important related concepts.
• Illustrate the usefulness of stochastic modelling by considering various different types of stochastic processes and how they can be applied in a range of scientific, engineering and other contexts.
• Examine how probablility distributons can be used to model system reliability and lifetimes.

• Understand the concepts of conditional and joint probabliity distribution, conditional expectation, covariance and correlation, use and interpret them.
• Understand the definitions of stochastic process and some related concepts and apply them in particular cases.
• Recognise situations where particular types of stochastic processes airse, and apply a sound working knowledge of the theory of each process to solves practical problems.
• Use simple survival analysis and life testing methods to model failure rates and investigation failure time distributions.

Teaching Methods:

Assessment:

Generic Skills Outcomes:

• Ability to apply knowledge of basic science and engineering fundamentals.
• Ability to communicate effectively, not only with engineers but also with the community at large.
• Ability to undertake problem identification, formulation and solution.

Content:

• Conditional probability distributions,
• Conditional expectation,
• Joint probability distributions,
• Covariance and correlation

• Definition and examples,
• Stationarity,
• Ergodicity

• Markov processes (discrete and continous parameter),
• Martingales
• Poisson processes,
• Queueing systems,
• Random walk,
• Application from engineering,
• Science and other fields.

• Series
• Parallel and complex systems
• Time to failure
• Hazard rate,
• Commonly arising distributions,
• Life testing, examples.

Reading Materials:

Nil

Textbooks:

Recommended Reading:

References: