hidden - assigned and confirmed by the Study Dept.
Date of registration:
27.09.2012
Date of assignment:
27.09.2012
Confirmed by Study dept. on:
04.12.2012
Date and time of defence:
26.06.2013 00:00
Date of electronic submission:
23.05.2013
Date of submission of printed version:
24.05.2013
Date of proceeded defence:
26.06.2013
Opponents:
doc. RNDr. Karel Zvára, CSc.
Guidelines
An expectation-maximization (EM) algorithm is used in statistics for finding maximum likelihood estimates of parameters in probabilistic models, where the model depends on unobserved latent variables. EM alternates between performing an expectation (E) step, which computes an expectation of the likelihood by including the latent variables as if they were observed, and a maximization (M) step, which computes the maximum likelihood estimates of the parameters by maximizing the expected likelihood found on the E step. The parameters found on the M step are then used to begin another E step, and the process is repeated. (in English or Czech)
References
[1] Dempster, Arthur; Laird, Nan; and Rubin, Donald (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, Vol. 39, Issue 1, Pages 1-38.
[2] Hogg, Robert; McKean, Joseph; and Craig, Allen (2005). Introduction to Mathematical Statistics. Upper Saddle River, NJ: Pearson Prentice Hall.
- assigned and confirmed by the Study Dept.