What are we going to do?
This app refers to the presentation 'Probabilistic
Knowledge Structures' by Heller and Wickelmaier. It will
demonstrate parameter estimation in probabilistic knowledge
structures:
- If you want to be reminded of the three estimation procedures, proceed to the tab 'Estimation methods' above (For a more comprehensive overview, see the presentation by Heller and Wickelmaier)
- If you are already confident in your knowledge about parameter estimation in probabilistic knowledge structures, you can directly proceed to 'Application' on the left and see how changes to the observed data and the assumed knowledge structure will affect parameter estimation
- In the end, you can test your knowledge with a small quiz
The three estimation methods
With a given knowledge structure and observed response patterns, there are three methods to estimate the parameters of a basic local independence model (BLIM):
- Maximum Likelihood (ML) Estimation
- Minimum Discrepancy (MD) Estimation
- Minimum Discrepancy ML Estimation (MDML)
Method | Principle | Pros | Cons |
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ML | estimates parameters that maximize the probability of the observed data |
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MD | assumes that any response pattern is generated by the knowledge state closest to it |
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MDML | ML estimation under certain MD restrictions |
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