732A90 Computational statistics
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.
Topic 5: Numerical model selection and hypothesis testingRead: 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 optimizationRead: 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