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If I want to generate multivariate Gaussian scale mixture samples with given location and covariance (characteristic matrix) of sample how will I generate that with any mixture distribution? … For example if I want to generate samples of Gaussian scale mixture with mean 0 and Covariance R let the mixture distribution be exponential or Laplace. How to generate that? …

I am given the values for mean, co-variance, initial_weights for a mixture of Gaussian Models. … I found scipy library that has GaussianMixture library. It basically takes input as sample values and calculate itself mean, co-variance. But for my case it is almost reverse. …

I want to generate (manually) a random sample in the Gaussian mixture model: $$f_{\theta}(x) = \sum_{k = 1}^{K}\pi_k f_{\mathcal N(\mu_k, \sigma^2_k)}(x)$$ Here is my work: theta = list(pi = c(p1,..., … return(cdf) } # find the root of the equation fmix = F(x) - u fmix = function(x,u) pnormmix(x,theta) - u my_root = function(x) uniroot(fmix, c(-1000, 1000), tol = 0.0001, u = x)$root # generate …

I am trying to program a Gaussian mixture regression using python, and the theano package. … In theano framework, I need to generate n shared variables for the regression weight matrices. …

Gaussian with this form: xx <- seq(-5,5,by=.01) plot(xx,0.25*dnorm(xx,-4,1.5)+0.75*dnorm(xx,0,1.5)+0.25*dnorm(xx,4,1.5) ,type="l",xlab="",ylab="" ) (With the distribution centered around … each value in the harmonic series) I can plot the distribution alright but can't generate one random value from that distribution to add it to the harmonic data. …

What are Gaussian scale mixture? Is it different from Gaussian mixture. … What is overall location and scale parameter of given Gaussian scale mixture and how to generate a samples of given $\mu$ and $\sigma^2$. …

I want to produce data with the following generative process which corresponds to a Mixture of Gaussians (MoG): $$ \begin{align} y\sim & Ber(p)\\ \mathbf{x}\sim & \mathcal{N}(\mu_y, \Sigma_y), \end{align …

How can I generate multi-dimensional data from a (estimated) Gaussian mixture pdf? In general, what would be ways to generate multi-dimensional data from a pdf? …

I know that sampling from a mixture of two normals involve the repetition of the following steps: 1) Generate a r.v. … $u \sim U(0,1)$ 2) If $u < p$, then generate a sample from $N(0,\sigma_1)$, if $u \geq p$, then from $N(0,\sigma_2)$, where $p$ is the mixture probability So, mirroring the non-mixture case, what I did …

Alex Graves created a model to generate hand writing sequences which use an LSTM (kind of Recurrent Neural Network) to predict the parameters for an mixture model. … Why bother with the mixture model? …

I generated some data, and then calculated the Gaussian Mixture model, and then came up with this figure: The histogram is the generated data and the red line is the gaussian mixture model. … Is this a possible fit with a Gaussian Mixture model and is my data to blame, or am I doing something wrong? Thanks in advance! …

Suppose I generate a Gaussian mixture model with $N$ Gaussian distributions $p(x) = \sum\limits_{i = 1}^N w_i \mathcal{N}(x;\mu_i, \Sigma_i)$ where $w_i$ are the weights. …

class-conditional (i.e., dependent on the value of the class variable Y) Gaussians. … Does mixture here mean is a Gaussian mixture model, which is a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown …

I use $H$ instead of $F$ because the distributions may belong to different families: for example, if $F$ is Gaussian, $H$ (which is a mixture of Gaussians) will in general be non-Gaussian. … Here is a specific example (although I am interested in the general case described above): $H$ is a mixture of two Gaussians Component 1 has weight $\pi_1 = 0.5$ and is $\mathcal{N}(\mu, \sigma^2_1)$ …

Let's assume that we generate some values by a mixture of two Gaussians. Now we want to find the parameters of the two Gaussians by likelihood maximisation. … The fitted model reflects the real generator perfectly (the data is generated by a mixture of two Gaussians). …