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In [[statistics]], an '''expectation–maximization''' ('''EM''') '''algorithm''' is an [[iterative method]] to find (local) [[maximum likelihood]] or [[maximum a posteriori]] (MAP) estimates of [[parameter]]s in [[statistical model]]s, where the model depends on unobserved [[latent variable]]s. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the [[Likelihood function#Log-likelihood|log-likelihood]] evaluated using the current estimate for the parameters, 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.
In [[statistics]], an '''expectation–maximization''' ('''EM''') '''algorithm''' is an [[iterative method]] to find (local) [[maximum likelihood]] or [[maximum a posteriori]] (MAP) estimates of [[parameter]]s in [[statistical model]]s, where the model depends on unobserved [[latent variable]]s. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the [[Likelihood function#Log-likelihood|log-likelihood]] evaluated using the current estimate for the parameters, 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.
在统计学中，期望最大化（EM）算法是一种寻找统计模型中（局部）极大似然或者最大后验（MAP）参数估计的迭代方法，其中的统计模型依赖于未观测到的潜在变量。EM迭代过程中交替执行期望（E）步和最大化（M）步；前者使用当前参数估计值建立对数似然函数的期望函数，后者计算能够最大化E步中获得的期望对数似然函数的参数。这些参数估计值将用于确定下一个E步中潜在变量的分布。