Expectation maximization
Algorithms AIMech:ExpectationMaximization DRAFT
Aliases: EM
Parameter estimation alternating expected complete-data log-likelihood evaluation and its maximization.
Identity Notes & Concept Scope
MachineLearningTask does not identify the alternating expected-log-likelihood and maximization estimation procedure.
Claims & Grounded Evidence
1 Claim
Mechanism
#mechanism
The E-step evaluates the expected complete-data log likelihood under current parameters; the M-step maximizes that expected objective.
⚠️ Curated Limitations & Negative Results
- This record supports method identity and the stated mechanism only; no universal performance or biological efficacy claim is curated.
- Proposed placement is an inference from the existing AIO hierarchy; maintainers must resolve task, algorithm and model scope before acceptance. These are procedures, not trained model instances; current AIO branch definitions mix abstraction levels.
Artificial Intelligence Ontology (AIO) Grounding
Mapped AIO Classes
| AIO Target Class | Mapping Relation | Mapping Rationale |
|---|---|---|
AIO:MachineLearningTask
Machine Learning Task
|
related_match | MachineLearningTask does not identify the alternating expected-log-likelihood and maximization estimation procedure. Mapping is local curation, not an upstream subclass assertion. |
Evidence Sources & Primary Literature
1 Source| Source ID | Publication / Document | Type / Access | Reference & Checksum |
|---|---|---|---|
method-source
|
Jeff A. Bilmes
|
author technical report
full_text
|
Curation Provenance
Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-11T03:01:34Z
Updated: 2026-10-11T03:01:34Z
Source File:
data/curated/ExpectationMaximization.yaml