B07
Course Coordinator: 
Tomoki Fukai
Statistical Methods
Description: 

(Course under review)

Aim: 
(Course under review)
Course Content: 

(Course under review)

  1. What is probability: frequentist and Bayesian views
  2. Statistical measures and Information theory
  3. Statistical dependence and independence
  4. Statistical testing
  5. Random numbers, random walks, and stochastic processes
  6. Regression and correlation analysis
  7. Analysis of variance I
  8. Analysis of variance II
  9. Statistical inference: maximum likelihood estimate and Bayesian inference
  10. Model validation and selection
  11. Experimental design
  12. Experimental design II
  13. Conditional probability
  14. Special probability densities and distributions
  15. Revision and conclusions
Course Type: 
Elective
Credits: 
2
Assessment: 
Problem sets, 60%; Final written test, 40%.
Text Book: 
All of Statistics - A Concise Course in Statistical Inference, by Larry Wasserman (2003) Springer
All of Nonparametric Statistics, by Larry Wasserman (2005) Springer
Reference Book: 
Pattern Recognition, 4 edn, by S. Theodoridis and K. Koutroumbas (2008) Academic Press
Neural Networks for Pattern Recognition, by Christopher Bishop (1996) Oxford University Press