Important announcement

Dear visitor,

Please note that this OpenCourseWare platform will be replaced at the end of 2026 by a new platform (under the same url ocw.tudelft.nl).

Courses and their contents that are currently available the platform will partly be migrated. URL’s for this content will change and content might be unavailable for some time. In addition, some courses will not be available anymore.

If you are linking to specific courses on this platform (for instance for your students), please take timely measures to avoid problems with access. Possible actions are:

  • Download the materials to your local platform (laptop, server)
  • For educators only (including TU Delft lecturers): Contact us at ocw@tudelft.nl and inform us what course you are using in what context (institute, program, period of use, number of students). We will research (in collaboration with the authors of the course) and decide if we will migrate the course, and if yes, can supply you with a new url.

Thank you.

Learning objectives: estimation

Course subject(s) 5. How precise is the estimate?

In the previous modules you learned how to estimate the parameters of interest based on the principles of (weighted) least squares or best linear unbiased estimation. But just knowing the estimate of the parameters is not enough – definitely you would also have to say something about the quality. Different types of errors will affect the quality of the result, as we will show in this module. The focus will be on the precision as an important measure of the quality.

Learning objectives:

By the end of the week you will be able to:

  • Apply the propagation laws of the mean and covariance
  • Assess the precision of the estimated parameters and derived products
Creative Commons License
Observation Theory: Estimating the Unknown by TU Delft OpenCourseWare is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Based on a work at https://ocw.tudelft.nl/courses/observation-theory-estimating-unknown.
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