Application of EM algorithm on missing categorical data analysis
Expectation- Maximization algorithm, or in short, EM algorithm is one of the methodologies for solving incomplete data problems sequentially based on a complete framework. The EM algorithm is a parametric approach to find the Maximum Likelihood, ML parameter estimates for incomplete data. The algori...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| التنسيق: | أطروحة |
| اللغة: | English |
| منشور في: |
2009
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | http://eprints.utm.my/id/eprint/12403/6/NorainiHasanMFS2009.pdf http://eprints.utm.my/id/eprint/12403/ |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| الملخص: | Expectation- Maximization algorithm, or in short, EM algorithm is one of the methodologies for solving incomplete data problems sequentially based on a complete framework. The EM algorithm is a parametric approach to find the Maximum Likelihood, ML parameter estimates for incomplete data. The algorithm consists of two steps. The first step is the Expectation step, better known as E-step, finds the expectation of the loglikelihood, conditional on the observed data and the current parameter estimates; say . The second step is the Maximization step, or Mstep, which maximize the loglikelihood to find new estimates of the parameters. The procedure alternates between the two steps until the parameter estimates converge to some fixed values. |
|---|