I have a problem with Matlab and probability density functions:
$x$ is a random variable, uniformly distributed between $[-0.5,0.5]$, so its probability density function is $p_x(t)=\text{rect}(t)$.
$w$ is also a random variable, randomly distributed with zero-mean and variance of 1. $p_w(t)=\frac{1}{\sqrt{2\pi}}\exp(\frac{-t^2}{2})$.
$x$ and $w$ are uncorrelated.
I have to find $p_{x+w}(t)$ and I know that this is equal to the convolution between $p_x(t)$ and $p_w(t)$:
$$
\int\frac{1}{\sqrt{2\pi}}\exp\Big(\frac{-(t-\tau)^2}{2}\Big)\text{rect}(\tau)d\tau = \int_{-0.5}^{0.5} \frac{1}{\sqrt{2\pi}}\exp\Big(\frac{-(t-\tau)^2}{2}\Big)d\tau \\= -\int_{t-0.5}^{t+0.5} \frac{1}{\sqrt{2\pi}}\exp\Big(\frac{-z^2}{2}\Big)dz = Q(t-0.5)-Q(t+0.5)
$$
My problem occurs when I plot this result with matlab:
plot(t,qfunc(t-0.5)-qfunc(t+0.5));
It's different from plotting the convolution of the two PDF's:
plot(t,decimate(conv(rectpuls(t),normpdf(t,0,1)),2));
Is there a way to understand which one of the two plots is the correct one?
Thank you.
Best Answer
Comment:
Let $U \sim \mathsf{Unif}(-.5, .5)$ and $Z \sim \mathsf{Norm}(0,1).$ Then $X = U + Z.$ I suppose that in your final result you use $Q$ for the standard normal CDF, often written $\Phi.$ However, what you have is a non-positive function. I think it should be $\Phi(x + .5)-\Phi(x - .5).$ [Thus, your analysis is correct except for a simple mistake in algebra.]
The following demonstration in R statistical software with a million observations $X$ shows a histogram consistent with that density function:
Note: In R
runif
andrnorm
sample from uniform and normal distributions andpnorm
is a normal CDF. I'm sorry not to use Matlab, but I do not have access to it.