Course home page
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.
Topics include:
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.
- Pdf-tiedosto
Exam points File
These are just the "raw" points of the exam. I will grade and combine the exam grade/points - with the weight 0.3 - with your assignment grade/points the first thing in January.
Final Grades Folder
Final grades derived from the assignment and exam points (weights 0.7 and 0.3, respectively). (I rounded points up in a few borderline cases.) The maximum points available is 55.2.
Points vs grades:
27 ... 1; 33 ... 2; 38.5 ... 3; 44 ... 4; 49.5 ... 5.