# Questions tagged [expectation-maximization]

An optimization algorithm often used for maximum-likelihood estimation in the presence of missing data.

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### Hidden Markov Model - Baum Welch algorithm initialization

Currently I'm working on a problem where I have a multidimensional, continuous sequence of observations $X$ that model my response variable $y$ with two states $0$ and $1$. I assume that this sequence ...
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### Is there a special case of the EM algorithm for exponential family distributions?

According to Wikipedia, the formal definition of the EM algorithm is The EM algorithm seeks to find the MLE of the marginal likelihood by iteratively applying these two steps: Expectation step (E ...
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### Areas of research in statistical machine learning

I'm reading from a book called Machine Learning: A Probabilistic Perspective by Kevin Murphy. Besides being somewhat challenging to understand, I feel that the earlier chapters (on probability, ...
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### Expected log likelihood for mixture components with differing support

I was hoping to use the EM algorithm to fit a mixture model in which the mixture components can have differing support. I've run into a problem during the M step because the expected log-likelihood ...
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### What are the variations of Expectation Maximization?

To explain my question better, I will use this analogy: In the case of the Gradient-Descent method, we have multiple variations/expansions for the main algorithm, like stochastic gradient descent (SGD)...
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### Regression where the matrix is drawn from a known distribution

I need to estimate the vector x, where Ax = b, given b is observed data and the columns in the matrix A are drawn from a known distribution. a and b are both formed of positive integers (or zero). For ...