# Questions tagged [gaussian-mixture]

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

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### Best approach to classifying 3-point trajectories?

I have a sample of about 300 subjects who have been measured at 3 different times (morning, afternoon, evening). The variable of interest can be assumed to be approximately normal. It appears that ...
245 views

### Whitening a mixture of Gaussians

Suppose I have a dataset that was sampled from a mixture of Gaussians: $$X \sim \sum_i w_i \mathcal{N}(\mu_i, \Sigma_i)$$ Technically, I can center and then whiten $X$ so that it has zero mean and ...
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### Algorithms for 1D Gaussian mixture with equal variance and noise cluster

I would like to fit a Gaussian mixture model to some data. The data is 1D and I want to constrain all the Gaussians to have equal variance. I would also like to have a uniform background noise cluster ...
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### EM-algorithm for two clusters (when one of the distributions is uniform)

I am having a hard time with the EM-algorithm. Here's the problem that I am trying to solve. Dealing with noisy annotations is a common problem in computer vision, especially when using ...
183 views

### Distribution of Distributions | percent point function

I have the following normal distribution (Primary Distribution): Mean = 7 Sigma = 0.5 and a list of other normal distributions (Secondary Distributions): (python list of tupels, each tupel ...
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### Can Gaussian Mixture Model Clustering tell me something about the distribution of my data?

I have 10,000 vectors originating from 5 separate classes (2,000 each). I use Gaussian Mixture Model clustering (in Python) to cluster the 10,000 vectors, telling the algorithm to cluster the data ...
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### comparing gaussian mixture models with and without regularization

I am using Gaussian mixture models for clustering a bunch of data sets. On some data sets a regularization value is often used/suggested (see the MATLAB example for Fisher's Iris data which sets ...
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### Assessing Gaussian mixture distribution by cross validation

I have a 10 dimensional random vector that I'm modelling with GMMs. I want to estimate the best number of mixtures ($K$) for my data via the following method: Divide the data to train (90%) and test (...
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### Need intuition - how do they simplify the Q function for gaussian mixture EM?

Background - I'm trying to follow section 7.2.4 in this EM tutorial. Basically the setup is I have a vector with 10 points $x$, and each of them can be assigned ($y$) to either Gaussian 1 or to ...
645 views

### Defining overlapping periods

I have a dataset containing the abundance of migrating bird species. In the figure below you can see that there are two "bell" shapes that are overlapping somewhere around September. One of the bell ...
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### How to know if my Gaussian mixture model has enough training data?

A somewhat soft question - I'm training a Gaussian mixture model (with the EM algorithm) on data of size $N$ ($N$ is typically between 4 and 64). How much samples do I need? obviously it depends on ...
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### GMM weighting change during marginalization

Suppose one has a multivariate Gaussian Mixture Model: $$\text{pdf}(\vec{x}) = \sum_{i=1}^N w_i \mathcal{N}(\mu^{(i)}, \Sigma^{(i)})$$ Suppose $\vec{x} = \{\vec{a},\vec{b}\}$ and we marginalize ...
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### Gaussian Mixture, sampling and interpretation

I came across the following which I have troubles understanding and would appreciate your help. Suppose we want to fit a Gaussian mixture model (GMM) to a $t$-dstribution with some degrees of ...
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### Bayesian Gaussian Mixture model

I am trying to fit basic Gaussian mixture with a Bayesian model. My likelihood function is Gaussian, with std=1, and the only parameter is the mean, chosen from $\{0,1,\dots,14,15\}$ and my prior is ...
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### Clustering data with Fourier series representation

We are analyzing temporal behavioral patterns across many users and we want to cluster users in order to understand "natural types of behavior". Our idea is to represent the data (672 bins for each ...
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### Gaussian Mixture and K-Means ?! a big challenge?

This is taken from Tom. Mitche Material as Old-Exam. I think the (2) is true and not (3). Who can verify me?
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### Re-estimating a probability distribution with additional priors

I have a 3D dataset with at least millions of data points (scatter events from atoms, approximately Gaussian). I am modeling this data with a Gaussian Mixture Model. The usual approach would be to ...
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