# Tagged Questions

EM stands for "expectation-maximization"; the algorithm is an iterative method for finding maximum likelihood estimates.

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### Can the EM algorithm be applied to my problem? Input data set is based on a function of parameter

I understand EM algorithm is often used for missing data/mixture problem. But can it be used to optimize a particular type of likelihood based on jointly fitting variables and transformations of those ...
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### EM maximum likelihood estimation for Weibull distribution

Note: I am posting a question from a former student of mine unable to post on his own for technical reasons. Given an iid sample $x_1,\ldots,x_n$ from a Weibull distribution with pdf  f(x) = k ...
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### Time dependant weights in hidden Markov models

I'm trying to modify a standard implementation of a continuous HMM with Gaussian Mixtures so that it internally gives more weight to newer observations in a time series. Essentially, I'm trying to ...
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### Averaged estimators in stochastic versions of EM

Recently I've been working EM algorithms for MAP estimation in a problem where the expectation is intractable, but the maximization is easy. Further, draws from the distribution in the E-step are ...
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### EM algorithm for mixture of Negative Binomial distributions

I am trying to derive the EM-algorithm of mixtures of negative binomial distribution $Neg\;Bin(r,p)$. I have the updating equations for updating the E-step as well as $p$ and the mixing coefficients ...
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### Does the EM algorithm require i.i.d?

The EM algorithm roughly has two steps. E-Step: Calculate the conditional expectation of the likelihood function given the data $x_1, . . . , x_n$ and the current estimates of parameters ...
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### Normal Data with missing values (EM Algorithm)

Suppose we have multivariate normal data (some missing entries, at random), with known covariance matrix. We would like to estimate the mean vector by the EM algorithm. How would one approach this? ...
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### How is the Gaussian mixture model used in a hidden Markov model for speech recognition?

How is the Gaussian mixture model used in a hidden Markov model for speech recognition? How do you apply the EM algorithm to estimate the parameters of each Gaussian? How to utilized the transcription ...
I have records of the time stamps of events coming from a small number of independent sources. Each source $S$ issues events at a given period $Ts$. Due to some jitter the time intervals between two ...