I think I have little difficulty in understanding the "Random Process". Here is a definition taken from Oppenheim's book.
In Section 7.3 we deﬁned a random variable X as a function that maps each outcome of a probabilistic experiment to a real number. In a similar manner, a real-valued CT or DT random process, X(t) or X[n] respectively, is a function that maps each outcome of a probabilistic experiment to a real CT or DT signal respectively,
termed the realization of the random process in that experiment. For any ﬁxed time instant t = t0 or n = n0, the quantities X(t0) and X[n0] are just random variables. The collection of signals that can be produced by the random process is referred to as the ensemble of signals in the random process.
So, in other word, can I say that, $X(t)$ (for $0<=t<=T) $ is just a collection of random variables that $X(0)$ is a single random variables, and $X(1)$ is another random variable? And could I say that, each $X(t_0)$ (where $t_o$ is just a number)is a sample taken from the PDF of $X$, and that each $X(t_o)$ is a realization of the random process?
I also have trouble trying to match it up with the matlab example, in which we run the 'normrand' command.
R = normrnd(mu,sigma,m,n,...)
For instance, if I run
n2 = normrnd(0,1,[1 5]) n2 = 0.0591 1.7971 0.2641 0.8717 -1.4462
Can I say that n2 is just a random process that each element of n2 array, n2, n2, n2, n2, n2, they are just samples taken from the normal gaussian distribution, and here, we are having 5 realizations of this gaussian process?