732A72 · 6 credits · Advanced level (A1N) · Statistik

Computational Statistics

Active · next start VT 2027

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After completion of the course the student should be able to:

account for how computer arithmetics affects statistical computations
develop computer implementations of standard methods for simulating from complex statistical distributions
develop computer implementations of commonly used deterministic and stochastic optimization methods in statistics and machine learning
select a suitable computer intensive uncertainty estimation methods for a given problem, and develop an implementation of the algorithm in a programming language
interpret the results obtained by various simulation and estimation methods
plan and carry out course work within given time frames

The course covers the following topics:

effect of computer arithmetics on statistical computations
basic methods for random number generation, including inverse CDF method and acceptance/rejection method
Monte Carlo methods for simulation and inference, including bootstrap and jackknife
Markov Chain Monte Carlo (MCMC) simulation, including Metropolis-Hastings and Gibbs samplers
introduction to unconstrained deterministic and stochastic optimization methods