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Expectation-maximization algorithm: Maximum likelihood, Maximum a posteriori, Latent variable, Constrained clustering, k-means clustering, Indicator f
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(Buch) |
Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!
| Lieferstatus: |
 i.d.R. innert 7-14 Tagen versandfertig |
| Veröffentlichung: |
April 2026
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| Genre: |
Schulbücher |
| ISBN: |
9786133895478 |
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EAN-Code:
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9786133895478 |
| Verlag: |
Omniscriptum |
| Einband: |
Kartoniert |
| Sprache: |
English
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| Dimensionen: |
H 220 mm / B 150 mm / D 5 mm |
| Gewicht: |
137 gr |
| Seiten: |
80 |
| Bewertung: |
Titel bewerten / Meinung schreiben
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| Inhalt: |
| Please note that the content of this book primarily consists of articles
available from Wikipedia or other free sources online. In statistics, an
expectation-maximization (EM) algorithm is a method for finding maximum
likelihood or maximum a posteriori (MAP) estimates of parameters in
statistical models, where the model depends on unobserved latent
variables. EM is an iterative method which alternates between performing
an expectation (E) step, which computes the expectation of the
log-likelihood evaluated using the current estimate for the latent
variables, and a maximization (M) step, which computes parameters
maximizing the expected log-likelihood found on the E step. These
parameter-estimates are then used to determine the distribution of the
latent variables in the next E step. |
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