The purpose of this course is to provide understanding of fundamental concepts and computational methods of stochastic simulations and models. After completing the assignments the student will have a library of (skeleton) algorithms used in stochastic simulation and understanding of how they work.
1. Simulating standard probability distributions.
2. Methods of simulating 'non-standard' distributions. Logarithmic binning.
3. Markov processes and stochastic models.
4. Monte Carlo (MC) method and Metropolis sampling.
5. Markov Chain Monte Carlo (MCMC) method; Gibbs and Metropolis-Hastings sampling.
6. Hamiltonian/Hybrid Monte Carlo (HMC) method.
The course is completed by doing programming assignments and a final exam. The main emphasis on assignments, they contribute 70 % the exam contributes 30 % to the grade. Minimum requirement to pass is 50 % of the total weighted points from the assignments and the exam.
Literature: Parts of the books Taylor, Karlin (newer edition Pinsky, Karlin): An Introduction to Stochastic Modeling (Academic Press), and Wilkinson: Stochastic Modelling for Systems Biology (CRC Press). Lecture notes and other distributed material.
Prerequisities: Basic programming skills. The programming language is Python. Jupyter notebook will be used.
For some practicalities etc., see Preliminaries in Materials.