Topic outline

  • General

    Please note: In order to have your registration for this or any other course accepted in Sisu, the course must be included in your study plan. Check that this is the case and also check that you have the latest version of your study plan in place. 


    Lecturer: Riku Linna (riku.linna@aalto.fi)

    Teaching Assistants:  Danh Phan (danh.phan@aalto.fi) and Thao Phung Duc (thao.phungduc@aalto.fi)


    Lectures, Tuesdays 12:15

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    https://aalto.zoom.us/j/66916419878

    Meeting ID: 669 1641 9878

    Exercise sessions, Thursdays 10:15

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    https://aalto.zoom.us/j/62533774506

    Meeting ID: 625 3377 4506

    While you are welcome to join, attendance is not required. The course is taken by returning weekly assignments and grading two or three assignment submissions weekly, using Peergrade. Instructions for joining the course Peergrade can be found under Materials. Deadlines for submission of solutions, peer grading and feedback can be found under Assignments. 20 % of the points come from peer grading, that is, how our grading is evaluated, and the rest from the graded solutions.

    Please note that since the assignments are peer-graded, unfortunately, you cannot come back next year and return possibly similar/same problems for grading.

    Please subscribe to the General discussion forum - under 'Forums' top right corner, for important announcements. Otherwise, Slack workspace, https://aalto-cmis-2021.slack.com, will be mainly used for communication. To join Slack, please use this invitation link. There you can ask questions, help others, etc. Please contact T.A via email if you face any problems in joining Slack. You are allowed and, in fact, encouraged to solve problems together. Just make sure that your solutions are not complete copy-pastes of someone else's.


    The purpose of this course is to provide an 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 an 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.

    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.

    The book Hossein Pishro-Nik, Introduction to Probability and Random Processes, freely available online will be used in parts of the course: https://www.probabilitycourse.com 


    Prerequisites: 

    Basic programming skills. The programming language is Python. Jupyter notebook will be used. 

    For some practicalities etc., see Preliminaries in Materials.