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https://blog.wtf.sg/2017/12/04/computing-log-normal-for-isotropic-gaussians

Computing Log Normal for Isotropic Gaussians

Consider a matrix $\mathbf{X}$ with rows of datapoints $\mathbf{x_i}$ which are $(n, d)$. The matrix $\mathbf{M}$ is made up of the $\boldsymbol{\mu}_j$ of $k$ different Gaussian components. The task is to compute the log probability of each of these $k$ components for all $n$ data points. In [1]: import theano import theano.tensor as T import numpy as np import time X = T.matrix('X') M = T.



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Computing Log Normal for Isotropic Gaussians

https://blog.wtf.sg/2017/12/04/computing-log-normal-for-isotropic-gaussians

Consider a matrix $\mathbf{X}$ with rows of datapoints $\mathbf{x_i}$ which are $(n, d)$. The matrix $\mathbf{M}$ is made up of the $\boldsymbol{\mu}_j$ of $k$ different Gaussian components. The task is to compute the log probability of each of these $k$ components for all $n$ data points. In [1]: import theano import theano.tensor as T import numpy as np import time X = T.matrix('X') M = T.



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https://blog.wtf.sg/2017/12/04/computing-log-normal-for-isotropic-gaussians

Computing Log Normal for Isotropic Gaussians

Consider a matrix $\mathbf{X}$ with rows of datapoints $\mathbf{x_i}$ which are $(n, d)$. The matrix $\mathbf{M}$ is made up of the $\boldsymbol{\mu}_j$ of $k$ different Gaussian components. The task is to compute the log probability of each of these $k$ components for all $n$ data points. In [1]: import theano import theano.tensor as T import numpy as np import time X = T.matrix('X') M = T.

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      Consider a matrix $\mathbf{X}$ with rows of datapoints $\mathbf{x_i}$ which are $(n, d)$. The matrix $\mathbf{M}$ is made up of the $\boldsymbol{\mu}_j$ of $k$ different Gaussian components. The task is to compute the log probability of each of these $k$ components for all $n$ data points. In [1]: import theano import theano.tensor as T import numpy as np import time X = T.matrix('X') M = T.
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      Computing Log Normal for Isotropic Gaussians
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      Consider a matrix $\mathbf{X}$ with rows of datapoints $\mathbf{x_i}$ which are $(n, d)$. The matrix $\mathbf{M}$ is made up of the $\boldsymbol{\mu}_j$ of $k$ different Gaussian components. The task is to compute the log probability of each of these $k$ components for all $n$ data points. In [1]: import theano import theano.tensor as T import numpy as np import time X = T.matrix('X') M = T.

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      Computing Log Normal for Isotropic Gaussians
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      Consider a matrix $\mathbf{X}$ with rows of datapoints $\mathbf{x_i}$ which are $(n, d)$. The matrix $\mathbf{M}$ is made up of the $\boldsymbol{\mu}_j$ of $k$ different Gaussian components. The task is to compute the log probability of each of these $k$ components for all $n$ data points. In [1]: import theano import theano.tensor as T import numpy as np import time X = T.matrix('X') M = T.
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