CS-E5795 - Computational Methods in Stochastics D, Lecture, 3.9.2024-9.12.2024

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Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

After the course you have a good command of selected central concepts and tools in stochastics.

You can program and use algorithms for analysing data and simulating and solving problems related to stochastic processes.

You have a thorough understanding of Monte Carlo-based methods and some of its advanced variants and a fair background for studying Bayesian statistical modelling.

 

Credits: 5

Schedule: 03.09.2024 - 09.12.2024

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Riku Linna

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content

  • valid for whole curriculum period:

    Fundamentals of relevant numerical mathematics, practical tools for data analysis (such as logarithmic binning), generation of random variables from different distributions,

    Markov chains, Monte Carlo methods (MCMC, Hamiltonian MC), some of the most important stochastic processes (e.g. Poisson, Gaussian, First-Passage)

     

Assessment Methods and Criteria

  • valid for whole curriculum period:

    The final grade is determined by the following weighted sum:  Computational assignments (70 %) + examination (30 %). Within computational assignments grading consists of the completed assignments (80 %) + peer reviewing (20 %). If this needs to be modified, it will be agreed upon at the start of the course. 

Workload

  • valid for whole curriculum period:

    Lectures 6x1.5 h, computer exercises appr. 7x1.5 h, independent studying (lectures, programming, exercise reports), final exam.

    Work load: Contact teaching 20 h + independent work studying for and completing the exercises 80 h + independent work doing the peer reviewing 10 h + preparing for and taking the exam 25 h = 135 h (5 cr).

DETAILS

Study Material

  • valid for whole curriculum period:

    Lecture notes and given articles. Reference books are Mark A. Pinsky, Samuel Karlin: An Introduction to Stochastic Modeling (2011 Elsevier), and Darren J. Wilkinson: Stochastic Modelling for Systems Biology, 2012 CRC Press.

Substitutes for Courses
Prerequisites
SDG: Sustainable Development Goals

    3 Good Health and Well-being

    13 Climate Action

    14 Life Below Water

    15 Life on Land

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Teaching Language: English

    Teaching Period: 2024-2025 Autumn I - II
    2025-2026 Autumn I - II

Rules of Conduct - Students´ Rights and Responsibilities (in force from 1 August 2020)

Aalto University Code of Academic Integrity and Handling Violations Thereof