Used to model systems or phenomena that evolve over time in a way that incorporates randomness or uncertainty.
Given a set $T$, a stochastic process ${X(t), t \in T}$ is a collection of random variables. That is, for each $t \in T$, $X(t)$ is a random variable.
- If T is countable, then the stochastic process is discrete-time (e.g Markov Chains).
- If T is an interval, then the stochastic process is continuous-time (e.g. Poisson Process.)
Continuous Random Variables