Faculty of Engineering

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This unit entry is for students who completed this unit in 2016 only. For students planning to study the unit, please refer to the unit indexes in the the current edition of the Handbook. If you have any queries contact the managing faculty for your course or area of study.

Monash University

0 points, SCA Band 2, 0.000 EFTSL

Postgraduate - Unit

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.



Organisational Unit

Department of Electrical and Computer Systems Engineering


Professor Emanuele Viterbo



  • First semester 2016 (Day)


This unit is available only to Engineering PhD students.


The unit introduces the fundamentals of statistical signal processing with emphasis on stochastic models, estimation theory, parametric and non-parametric modelling and least squares methods.

After a review of basic probability and random processes, the use of stochastic models for real world signals is illustrated. A family of algorithms for the creation, efficient representation and effective modelling is presented.

Specifically, linear stochastic models are presented and the importance of correlation structure in deriving the parameters of such models is illustrated.

The unit also covers how parametric and non-parametric models as well as statistical techniques are used to extract information from data signals corrupted by noise. The concept of estimation from real world data is presented, as opposed to the basic analysis of signals, transfer functions and power spectra. In particular, the fundamentals of linear estimation theory and optimal filtering to design advanced signal processing algorithms are presented.


On successful completion of this unit, students will be able to:

  • describe various models for real world signals
  • analyse the performance of a range of estimation methods
  • simulate a wide range of stochastic signal processing algorithms and interpret the results
  • design specific algorithms for processing real world signals such as audio, financial data and biomedical data.


Continuous assessment: 50%
Examination (3 hours): 50%

Students are required to achieve at least 45% in the total continuous assessment component and at least 45% in the final examination component and an overall mark of 50% to achieve a pass grade in the unit.

Workload requirements

3 hours lectures, 3 hours of tutorial/laboratory, and 6 hours of private study per week.

See also Unit timetable information

Chief examiner(s)