Computational Statistics

Updated: about 1 month ago
Deadline: 01 Sep 2019

Computational Statistics
PhD School at the Faculty of SCIENCE at University of Copenhagen
Target group
Knowledge:
- fundamental algorithms for statistical computations
- R packages that implement some of these algorithms or are useful for developing novel implementations.
Skills: Ability to
- implement, test, debug, benchmark, profile and optimize statistical software.
Competences: Ability to
- select appropriate numerical algorithms for statistical computations
- evaluate implementations in terms of correctness, robustness, accuracy and memory and speed efficiency.
Content
- Maximum-likelihood and numerical optimization.
- The EM-algorithm.
- Stochastic optimization algorithms.
- Simulation algorithms and Monte Carlo methods.
- Nonparametric density estimation.
- Bivariate smoothing.
- Numerical linear algebra in statistics. Sparse and structured matrices.
- Practical implementation of statistical computations and algorithms.
- R/C++ and RStudio statistical software development.

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