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Advanced Computational Statistics


Status Active - open for registrations
School IDA-gemensam (IDA)
Division STIMA
Owner Frank Miller
Homepage http://www.adoptdesign.de/frankmillereu/adcompstat2023.html

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Course plan

Recommended for

The course is intended for Ph.D. students from Statistics or a related field (e.g. Mathematical Statistics, Engineering Science, Quantitative Finance, Computer Science).

The course was last given

In this form, the course is new. The optimisation part of this course is a part of the course Optimisation Algorithms in Statistics given in Fall 2020 and Spring 2021.

Intended Learning Oucomes

On completion of the course, the student is expected to be able to:
• Demonstrate knowledge of principles of computational statistics
• Explain theoretical and empirical methods to compare different algorithms
• Design and organize algorithms for optimisation, integration, and simulation of distributions
• Solve statistical computing problems using advanced algorithms
• Adapt a given optimisation, integration, or simulation method to a specific problem
• Assess, compare and contrast properties of alternative optimisation, integration, and simulation methods
• Critically judge different methods for optimisation, integration, and simulation
• Ability to choose an adequate method for a given statistical problem


Accepted to a doctoral program in Sweden in Statistics or a related field. Knowledge about Statistical Inference (e.g. from the
Master's level) and familiarity with a programming language (e.g. with R) is required.


The course contains fundamental principles of computational statistics. Focus is on:
• Principles of gradient based and gradient free optimisation including stochastic optimisation and constrained optimisation
• Introduction to convergence analysis for stochastic optimisation algorithms
• Statistical problem-solving using optimisation, including maximum likelihood, regularized least squares, and optimal experimental designs
• Principles of numerical integration
• Principles of statistical simulation
• The bootstrap method
• Statistical problem-solving using simulation techniques including generation of Monte Carlo estimates, their confidence intervals, and posterior distributions


• Givens GH, Hoeting JA (2013). Computational Statistics, 2nd edition. John Wiley & Sons, Inc., Hoboken, New Jersey.
• Goodfellow I, Bengio Y, Courville A (2016). Deep Learning. MIT Press, http://www.deeplearningbook.org. Focus on Chapter 4, 5, and 8.
• Further literature including research articles and other learning material will be provided in the course


Lectures and problem sessions will be partly in class (one session in Linköping, one in Stockholm) and partly online.


The Intended Learning Oucomes will be graded by a written exam and/or hand-in solutions to assignments. The grades given: Pass/Fail.


Frank Miller



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