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Course Catalog 2010-2011
MIT-3016 Analysis of Measurement Data 1, 7 cr |
Person responsible
Risto Ritala
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
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Requirements
Examination and computer exercises. 20 % of homework exercises.
Principles and baselines related to teaching and learning
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Learning outcomes
Develops capability to assess properties of systems by analyzing and computing characteristics of stochastic measurement signals and pairs of signals.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Measurement as reflecting reality, probabilistic view. Measurement uncertainty. | Principles of Bayesian statistics and Bayesian measurement information theory. | |
| 2. | One and two variable normal distributions and their use in abnormality detection and state recognition. | ||
| 3. | Identification of statistical models by maximum likelihood or least mean squares. Motivation for identification methods. | General maximum likelihood / maximum a posteriori identification. | |
| 4. | Covariance function, spectrum and their non-parametric estimation. Effect of sampling on estimates. | Introduction to parametric spectrum estimation. Introduction to time series analysis. | Relationship between cross-covariance/spectrum and joint probability density function of time series. |
| 5. | Spectral analysis of linear dynamic and stochastic systems. |
Evaluation criteria for the course
Exam 0-30 points. 20% of homework exercises mandatory. By doing homework exercises, up to 5 bonus points for exam. Particpation in 4/5 computer exercises mandatory.
Assessment scale:
Numerical evaluation scale (1-5) will be used on the course
Study material
| Type | Name | Author | ISBN | URL | Edition, availability, ... | Examination material | Language |
| Lecture slides | Suomi |
Prerequisite relations (Requires logging in to POP)
Correspondence of content
There is no equivalence with any other courses
More precise information per implementation
| Implementation | Description | Methods of instruction | Implementation |
| Lectures, computer exercises and homework exercises on measurement information | Lectures Excercises Practical works |
Contact teaching: 0 % Distance learning: 0 % Self-directed learning: 0 % |