I'm reading an article and the author write something that i can't understand: "it is easy to see that" $$\int_0^t\int_{\mathbb{R}^2}K(t-s,x-y)u^p(s,y)dyds\geq \int_0^t\left(\int_{\mathbb{R}^2}K(t-s,x-y)u(s,y)dy\right)^pds, p>1$$ where $$K(t,x_1,x_2)=\dfrac{\sqrt{2}}{2\pi t^{3/2}}e^{-\frac{x_1^2}{2t}-\frac{x_2^2}{t^2}},$$ and $u$ is a function non-negative, continuous and bounded.
I can't see the "jump". I'm trying something with the Minkowski's inequalities for integrals but i didn't get anything so far. Can anyone help me, please?
This is use of Jensen's inequality.
You can easily check that $K$ is probability density function of 2-dimensional normal distribution, and function $f(x) = |x|^p$ is convex for $p>1$.
Inner integral on the LHS is actually expected value $\mathbb E [u(Z_1, Z_2)^p]$ for some appropriate normal variables, and we have $$ \mathbb E [u(Z_1, Z_2)^p] \geq (\mathbb E [u(Z_1, Z_2)])^p. $$