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I have a factor analysis model defined by:

$x = m + Wz + e$

where $x$ is a p-dimensional visible variable, $m$ is a constant vector, and $z$ is a $n$-dimensional Gaussian latent variable with $z$ ~ $N(0, I)$, $W$ is a $p\times m$ matrix and $e$ is a $p$-dimensional with $e$ ~ $N(0, \Psi$) - it is the factor loadings matrix. $Z$ and $e$ are independent.

$p(x)$ is defined by the model is Gaussian, but how do I find its mean?

Also, what is the explicit joint distribution of $p(z, x)$?

Andre Silva
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  • You keep reusing the same letters for different meanings ($m, p$), but the mean of $x$ is clearly $m$, because the rest have mean $0$. – Aniko Apr 27 '12 at 21:24
  • Peter, you need to take a crash course in [multivariate normal distribution](https://en.wikipedia.org/wiki/Multivariate_normal_distribution) and do [some exercises](http://www.amazon.com/Matrix-Algebra-Econometric-Exercises-Vol/dp/0521537460) to understand the derivations involved. This was discussed on CV [before](http://stats.stackexchange.com/q/30588/5739), and you can also find [helpful handouts](http://www.math.uiuc.edu/~r-ash/Stat/StatLec21-25.pdf) from various multivariate statistics classes. – StasK Jun 26 '13 at 13:17

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