I would need a closed-form expression for the convolution integral of these two probability density functions:
$$p_1(x) = (\lambda_1\lambda_2/(\lambda_1-\lambda_2))\cdot(\exp(-\lambda_2x)-\exp(-\lambda_1x))$$
$$p_2(x) = \frac{C}{\sqrt{2\pi}}{\frac{1}{x^{3/2}}}\exp\left(\frac{-C^2}{2x}\right);$$
for $x\geq0$, while $p_{1,2}(x) = 0$ for $x<0$
where
$p_1(x)$ is the pdf of the hypoexponential distribution with parameters $\lambda_1$ and $\lambda_2$
$p_2(x)$ is the pdf of the Lévy distribution with parameters $c = C$ and $\mu = 0$
The aim is to obtain the pdf $p_3(x)$ of the sum of two independent random variables with pdf $p_1(x)$ and $p_2(x)$, respectively.
$$p_3(x) = \operatorname{heaviside}(x)\int_0^{x}{p_1(t)p_2(x-t)dt}$$
I was not able to get a closed-form solution with Matlab symbolic toolbox, which produces:
$$p_3(x) = \operatorname{heaviside}(x)\int_0^{x}{ -\frac{\sqrt{2}\,\sqrt{C}\,l_{1}\,l_{2}\,{e}^{\frac{C}{2\,\left(t-x\right)}}\,\left({e}^{-l_{1}\,t}-{e}^{-l_{2}\,t}\right)}{2\,\sqrt{\pi }\,\left(l_{1}-l_{2}\right)\,{\left(x-t\right)}^{3/2}}dt }$$
Can someone solve it manually or confirm that it does not have a closed-form?
Mathematica gives the result
$$\int\limits_0^x p_1(t)\,p_2(x-t)\,dt=\frac{(\lambda_1 \lambda_2)}{2 (\lambda_1-\lambda_2)} \left(e^{-\lambda_2 x} \left(e^{i C \sqrt{2 \lambda_2}}\ \text{erfc}\left(\frac{C}{\sqrt{2 x}}+i \sqrt{\lambda_2 x}\right)+e^{-i C \sqrt{2 \lambda_2}}\ \text{erfc}\left(\frac{C}{\sqrt{2 x}}-i \sqrt{\lambda_2 x}\right)\right)-e^{-\lambda_1 x} \left(e^{i C \sqrt{2 \lambda_1}}\ \text{erfc}\left(\frac{C}{\sqrt{2 x}}+i \sqrt{\lambda_1 x}\right)+e^{-i C \sqrt{2 \lambda_1}}\ \text{erfc}\left(\frac{C}{\sqrt{2 x}}-i \sqrt{\lambda_1 x}\right)\right)\right)$$
which is valid for $\lambda_1,\lambda_2,C,x>0\land\lambda_1\ne\lambda_2$.