- Stochastic or Random Processes and Time Series
Miguel F. Acevedo · 2012
A stochastic or random process is a sequence of random variables X(t) with a distribution that may vary with time t and a joint distribution for the entire sequence. For example, a Gaussian process is a sequence of normally distributed variables with a joint distribution that is also normal. The process is stationary if the distribution is constant with time t. To be less restrictive, we can make a weaker statement considering the process stationary if the mean and variance are constant. For example, a Gaussian process dened by identical independent normal variables with N(0, σ) is stationary. This denition is similar to the one of a stationary spatial random variable, as we will discuss in Chapter 8.