This definite integral relates to Bayesian statistics and is a problem that needs to be solved in order to show that the $\text{Gamma}(\alpha,\beta)$ distribution is a (prior) conjugate family for the $\text{Poisson}(\lambda)$ distribution. Now, I know there are ways to prove this by means of proportionality arguments, and then identify the posterior distribution to make the necessary conclusion, but I want to do it the hard (but more intuitive) way. So I have,
$$ \int_0^\infty \lambda^{k+\alpha-1}\text{e}^{-(\beta+1) \lambda} \, d\lambda $$
where $k$ is an integer $\geq0$ and $\alpha, \beta > 0$. In a "normal" situation where $\lambda$ is raised to the power of an integer, I would be able to solve it by integration of parts, but know I am not so sure. Any clues?
Edit
I use the hints user NHL provided:
\begin{align} \int_0^\infty \lambda^{k+\alpha-1}\text{e}^{-(\beta+1)\lambda} \, d\lambda &= \big\{\mu = (\beta+1)\lambda,\, d\mu = (\beta+1) \, d\lambda \big\} \\[3mm]&= \frac{1}{(\beta + 1)^{k+\alpha}} \int_0^\infty \mu^{k+\alpha-1} \text{e}^{-\mu} \, d\mu \\[3mm] &= \big\{ \Gamma(z)\triangleq \int_{0}^{\infty}x^{z-1}\text{e}^{-x} \, dx\big\}\\[3mm] &= \frac{\Gamma(k+\alpha)}{(\beta + 1)^{k+\alpha}}. \end{align}