likelihood, maximum likelihood concept
| Definition | The likelihood of a model is the probability it assigns to the observed data, and maximum likelihood chooses the parameters that make it largest, which is minimizing the total surprise the model assigns, the cross-entropy with the sample standing in for the truth. Primer S section S.10. Also likelihood. |
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| Example | Seven heads in ten flips have likelihood p to the seventh times (1 minus p) cubed, largest at p = 0.7. |
| Book | Data Mining as Observation, draft 0.2, commit f3914f0; entry id likelihood, kind concept. |
| Status | no ledger row names this entry. Corrections: none recorded. |
| Defining equation | none |
| Assumptions and scope | none |
| Prior art | none recorded |
| Evidence | none |
| Reviewed | semantic review 2026-09-09; generated 2026-09-10 from records at the commits on the provenance page. |
Equation
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Ledger
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First stated
Primer S section S.10 of Data Mining as Observation, added in draft 0.3 (2026-09-09) for the ECE 514 readers whose first courses are far behind. The idea is standard and TSK Appendix C covers it at length.
Measurements
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Failures and corrections
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Invariance envelope
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Machine checked
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Used in
Data Mining as Observation primer S, chapters 5, 6.
Related
cross-entropy; entropy; estimator, unbiased.
See also
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Status
Generated 2026-09-10 by encyclopedia/generate.py; book
at observation-data-mining f3914f0; the commit of every record is listed
in the encyclopedia’s provenance.