Intereting Posts

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Say you have $k$ i.i.d. normal random variables with some mean $\mu$ and variance $\sigma^2$ and you multiply them all together. What is the density function of the result?

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Let $X_k = Z_1 Z_2 \cdots Z_k$, where $Z_i$ are iid normal r.v. with mean $\mu$ and variance $\sigma^2$. The precise density would be hard to come by, but moments are easy to compute:

$$

\mathbb{E}(X_k^r) = \mathbb{E}(Z_1^r) \mathbb{E}(Z_2^r) \cdots \mathbb{E}(Z_k^r) = \left(m_r(Z)\right)^k

$$

Here is some simulation in *Mathematica*:

```
ProductNormalHistogram[k_Integer?Positive, \[Mu]_, \[Sigma]_] :=
Block[{x},
Histogram[
RandomVariate[
TransformedDistribution[Array[x, k, 1, Times],
Array[x, k] \[Distributed]
ProductDistribution[{NormalDistribution[\[Mu], \[Sigma]
], k}]], 10^5], Automatic, "PDF", ImageSize -> 250]]
```

For the case of $\mu=0$ and $\sigma = 1$, look-up product-normal distribution for some analytic results.

I think this paper would interest you:

The Distribution of Products of Beta, Gamma and Gaussian Random VariablesM. D. Springer and W. E. Thompson

SIAM Journal on Applied Mathematics , Vol. 18, No. 4 (Jun., 1970), pp. 721-737Published by: Society for Industrial and Applied Mathematics

Article Stable URL: http://www.jstor.org/stable/2099424

**Hint–Elementary(!)**

Prove that:

For continuous random variables $X$ and $Y$ with joint density $f$, the density of $Z=XY$ is given by $$f_Z(z)=\int_{-\infty}^\infty \frac 1 {|x|} f\left(x,\frac z x\right) \mathrm{d} x$$

Can you generalize this to $k$ of them by induction?

But, for me this looks like a far fetched idea in that, this is more elementary. (I have never had to bother about more than $2$ for computing by hand.)

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