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FIK3617 Probability and Stochastic Processes for Engineering Applications 9.0 credits

Course offerings are missing for current or upcoming semesters.
Headings with content from the Course syllabus FIK3617 (Spring 2016–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

Review of Basic Probability:  Probability spaces, random variables, distribution and density functions, expectation, characteristic functions, conditional probability, conditional expectation.
Sequences of Random Variables:  Convergence concepts, laws of large numbers, central limit theorem.
Basic Concepts of Stochastic Processes:  General concepts, types of stationarity, properties of stochastic processes, systems with stochastic inputs.
Random Processes in Linear Systems:  Spectral analysis of random processes in linear systems, spectral representation and Fourier transforms.
Special Processes:  Markov processes, Wiener Process, Poisson processes, shot noise, thermal noise.
Spectral Representation of Random Processes:  White-noise integrals, expansion of random processes
Applications:  Signal detection and parameter estimation

Intended learning outcomes

The course is a first graduate (PhD) course in probability and stochastic processes. The course aims at providing the student with a good review of probability theory, and random variables. The course then has its focus on stochastic processes with special attention on applications in wireless communication and signal processing.
After the course the student should be able to:
- model signals and phenomena in a probabilistic manner.
- optimize performance in statistical terms.
- use analytical tools that are useful in the study of stochastic models that appear in wireless communications and other engineering fields.
- predict system performance using statistical reasoning, and verify it using numerical methods.

Literature and preparations

Specific prerequisites

The course is a first year doctoral course

Basic university level course in probability and statistics.

Recommended prerequisites

Basic university level course in probability and statistics.

Equipment

No information inserted

Literature

Davenport, “Probability and Random Processes”, McGraw-Hill 1970, Classic textbook reissue 1987

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

P, F

Examination

    Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

    The examiner may apply another examination format when re-examining individual students.

    Pass/Fail

    Other requirements for final grade

    To pass the course you need to correctly solve 75% or more of the homework problems, or written final exam.

    Opportunity to complete the requirements via supplementary examination

    No information inserted

    Opportunity to raise an approved grade via renewed examination

    No information inserted

    Examiner

    Ethical approach

    • All members of a group are responsible for the group's work.
    • In any assessment, every student shall honestly disclose any help received and sources used.
    • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

    Further information

    Course room in Canvas

    Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

    Offered by

    Main field of study

    This course does not belong to any Main field of study.

    Education cycle

    Third cycle

    Add-on studies

    No information inserted

    Postgraduate course

    Postgraduate courses at EECS/Communication Systems