Keywords
Em Algorithm

# ,Em Algorithm,EM algorithms,EM algorithmic,EMS Algorithm,EMS algorithms

Em Algorithm
Publications: 7,497| Citation Count: 108,071
Stemming Variations: EM algorithms, EM algorithmic, EMS Algorithm, EMS algorithms

## Definition Context (5)

• The EM algorithm is a popular and useful algorithm for finding the maximumlikelihood estimator in incomplete data problems. Each iteration of thealgorithm consists of two simple steps: An E-step, in which a conditional expectationis calculated, and an M-step, where the expectation is maximized.In some problems, however, the EM algorithm cannot be applied since theconditional expectation required in the E-step cannot be calculated...

### Soren Feodor Nielsen. On simulated EM algorithms

• The EM algorithm is a popular method for parameter estimation in a variety of problems involving missing data. However, the EM algorithm often requires significant computational resources and has been dismissed as impractical for large databases...

### Bo Thiesson, et al. Accelerating EM for Large Databases

• The EM algorithm is a popular method for parameter estimation in a variety of problems involving missing data. However, the EM algorithm often requires significant computational resources and has been dismissed as impractical for large databases...

### Bo Thiesson, et al. Accelerating EM for Large Databases

• The EM algorithm is a generic tool that offers maximum likelihood solutions when datasets are incomplete with data values missing at random or completely at random. At least for its simplest form, the algorithm can be rewritten in terms of an ANCOVA regression specification. This formulation allows several analytical results to be derived that permit the EM algorithm solution to be expressed in terms of new observation predictions and their variances...

### Daniel A. Griffith, et al. SOME SIMPLIFICATIONS FOR THE EXPECTATION MAXIMIZATION (EM) ALGORITHM: ...

• The EM algorithm is a popular iterative method for estimating parameters in the latent class model where at each step the unknown parameters can be estimated simply as weighted sums of some latent proportions...

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