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732A90 Computational statistics

Course information

This course is an obligatory course in the master's program Statistics and Data Mining. Other students can apply for it as a single-subject course.

The target student group are those who are familiar with statistics, have good mathematical background and practical experience of programming in some computer language.

The programming language of the course is R.

The course litterature is:

  • Computational Statistics (CS) by James E. Gentle, ISBN 9780387981437.
  • Selected scientific papers and manuals

Topic 1: Computer arithmetics.

Read: CS, Ch.2, 5.1 and 5.2.
Single subject course students: Self study of R syntax and basic functions, read and test the code from here

Topic 2: Optimization.

Read: CS, Ch 5.4, 6.1 and 6.2

Topic 3: Random number generation.

Read: CS, Ch. 7.1-7.3

Topic 4: Monte Carlo methods, MCMC.

Read: CS, ch. 7.3-7.4, Ch. 11 of Computational Statistics Handbook with Matlab by Martinez (available here), and handouts

Topic 5: Numerical model selection and hypothesis testing

Read: CS, ch. 12, 13 and handouts. Those of you who are not familiar with hypothesis testing, read Ch. 6 in Computational Statistics Handbook with Matlab by Martinez

Topic 6: EM algorithm and stochastic optimization

Read: CS, Ch 6.3, "Pattern Recognition" by Bishop, Ch 9.2-9.3, paper "Large-scale machine learning with stochastic gradient descent" by Bottou (2010)

Page responsible: Krzysztof Bartoszek
Last updated: 2016-06-17