The expectation-maximization algorithm is an approach for performing maximum likelihood estimation in the presence of latent variables. It does this by first estimating the values for the latent variables, then optimizing the model, then repeating these two steps until convergence.

What is Expectation Maximization algorithm?

The expectation-maximization algorithm is an approach for performing maximum likelihood estimation in the presence of latent variables. It does this by first estimating the values for the latent variables, then optimizing the model, then repeating these two steps until convergence.

Is MLE Bayesian?

This is the difference between MLE/MAP and Bayesian inference. MLE and MAP returns a single fixed value, but Bayesian inference returns probability density (or mass) function.

What is expectation maximization in machine learning?

The Expectation-Maximization algorithm aims to use the available observed data of the dataset to estimate the missing data of the latent variables and then using that data to update the values of the parameters in the maximization step.

What are the steps of EM algorithm?

Expectation step (E – step): Using the observed available data of the dataset, estimate (guess) the values of the missing data. Maximization step (M – step): Complete data generated after the expectation (E) step is used in order to update the parameters. Repeat step 2 and step 3 until convergence.

What is EM algorithm explain it?

The Expectation-Maximization (EM) algorithm is a way to find maximum-likelihood estimates for model parameters when your data is incomplete, has missing data points, or has unobserved (hidden) latent variables. It is an iterative way to approximate the maximum likelihood function.

What is the difference between K-means and EM?

EM and K-means are similar in the sense that they allow model refining of an iterative process to find the best congestion. However, the K-means algorithm differs in the method used for calculating the Euclidean distance while calculating the distance between each of two data items; and EM uses statistical methods.

What is Bayesian decision theory?

Bayesian decision theory refers to the statistical approach based on tradeoff quantification among various classification decisions based on the concept of Probability(Bayes Theorem) and the costs associated with the decision.

What is the difference between k-means and EM?

What is EM algorithm in data mining?

In data mining, expectation-maximization (EM) is generally used as a clustering algorithm (like k-means) for knowledge discovery. In statistics, the EM algorithm iterates and optimizes the likelihood of seeing observed data while estimating the parameters of a statistical model with unobserved variables.

What is expectation maximization EM for soft clustering?

The expectation maximization or EM algorithm can be used to learn probabilistic models with hidden variables. Combined with a naive Bayes classifier, it does soft clustering, similar to the -means algorithm, but where examples are probabilistically in classes.

What is expectation maximization for missing data?

Expectation maximization is applicable whenever the data are missing completely at random or missing at random-but unsuitable when the data are not missing at random. To illustrate, consider the following extract of data. Conceivably, individuals who do not answer questions about depression tend to be very depressed.