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Online course materials for MATH48091

Statistical Computing

Unit code: MATH48091
Credit Rating: 15
Unit level: Level 4
Teaching period(s): Semester 1
Offered by School of Mathematics
Available as a free choice unit?: N



Additional Requirements

MATH48091 pre-requisites

Students are not permitted to take more than one of MATH38091 or MATH48091 for credit in the same or different undergraduate year.  Students are not permitted to take MATH48091 and MATH68091 for credit in an undergraduate programme and then a postgraduate programme.


To introduce the student to computational statistics, both the underlying theory and the practical applications.


Computers are an invaluable tool to modern statisticians. The increasing power of computers has greatly increased the scope of inferential methods and the type of models which can be analysed. This has led to the development of a number of computationally intensive statistical methods, many of which will be introduced in this course.

Learning outcomes

On successful completion of this course unit students will be able to

  • appreciate the usefulness of computational methods in modern statistics;
  • understand the basic ideas underpinning the theory;
  • be able to apply the methodology to standard problems.

Assessment methods

  • Other - 50%
  • Written exam - 50%

Assessment Further Information

  • Four coursework projects: 50%
  • End of semester written examination (2 hours): 50%


  • Introduction [1]
  • Simulating random variables: inversion of the cdf; rejection sampling; transformations; ratio of uniforms. [4]
  • Monte Carlo integration [1]
  • Variance Reduction: importance sampling; control variates. [2]
  • Nonparametric bootstrap methods; the Jackknife. [6]
  • Nonparametric density estimation: the histogram; kernel density estimation. [4]
  • Nonlinear regression: model specification; least squares estimation; Gauss-Newton algorithm. [2
  • Nonparametric regression: moving average estimator. [2]
  • EM algorithm: data augmentation; the multinomial model; mixture distributions, censored data, standard errors, Monte-Carlo EM. [6]

Recommended reading

  •  Rizzo, M.  Statistical Computing with R.  Chapman & Hall
  • Ripley, B.D.  Stochastic Simulation.  Wiley.
  • Efron, B. and Tibshirani, R. An introduction to the bootstrap.  Chapman & Hall
  • Jones, M. and Wand, M.  Kernel smoothing.  Chapman & Hall

Feedback methods

Feedback tutorials will provide an opportunity for students' work to be discussed and provide feedback on their understanding.  Coursework also provides an opportunity for students to receive feedback.  Students can also get feedback on their understanding directly from the lecturer, for example during the lecturer's office hour.

Study hours

  • Lectures - 22 hours
  • Practical classes & workshops - 22 hours
  • Independent study hours - 106 hours

Teaching staff

Peter Foster - Unit coordinator

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