# Questions tagged [gaussian-mixture]

A type of mixed distribution or model which assumes subpopulations follow Gaussian distributions.

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### How to get number of iterations in EM-algorithm using R mclust gaussian mixture model

I am clustering data using the mclust function from the R mclust package. I am struggling to get the number of iterations the EM ...
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### How to calculate log likelihood for gaussian mixture model

I'm trying to check the calculation of the log likelihood of a 2 component Gaussian Mixture Model using optim, but I get the wrong answer (it should return mu, sigma, alpha actual). The log ...
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### Fitting a mixture model distribution to kurtotic data

I need to fit a parametric distribution to data that has non-zero (unknown) kurtosis. First I tried to fit a Pearson type VII / Student's t, but the fitting is especially poor in the two tails, ...
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### Building a mixture model that fits well to the tail of a kurtotic distribution

I need to fit a distribution to data that has non-zero (unknown) kurtosis. I tried to fit a Pearson type VII / Student's t, but the fitting is especially poor in the two tails, possibly due to less ...
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### Are Neural Networks Mixture Models?

To my understanding, Gaussian Mixture models are a set of parameterized gaussian distributions that collectively describe an entire, aggregate distribution. ^ from McGonagle et al Also to my ...
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### Can a variational autoencoder be interpreted as a mixture of Gaussians?

In a variational autoencoder (VAE) we have an encoder network $E_{\phi}$ that maps inputs $x$ to the distribution parameters of the approximate posterior $q_{\phi}(z \vert x)$. Most commonly we model ...
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### M step EM algorithm in Mixture Models. Expected value of the indicator variable under the posterior [closed]

I am not able to solve the following expectation. In the EM algorithm, the first step in the M step is to compute the expected value of $\log p(x,z)$ where $x$ are observations and $z$ indicator ...
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### Mixture or Convolution

tl;dr is final paragraph at the bottom. I have read the posts explaining the differences between mixture distributions and convolutions of distributions, but am having a hard time understanding which ...
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### Initialisation strategies for learning Hidden Markov Models

I used hmmlearn library to initialize an HMM (Hidden Markov Model). sampled observations from the HMM, and used the sampled data to re-estimate the parameters of ...
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### Gaussian mixture models for image matrix not determining E step

I want to calculate responsibility for each of the data points, for the given MU, SIGMA and PI. ...
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### Compute membership probabilities in E-step of EM algorithm with log-densities instead of densities

As an exercise I have implemented the EM algorithm for Gaussian mixtures, however, I have the problem that in high dimensions the densities of data points become so small that I get a numerical ...
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