A) A linear equation. B) A collection of random variables indexed by time or space. C) A constant value. D) A deterministic function.
A) It exhibits periodic behavior. B) Future behavior does not depend on past history given the present. C) Past behavior strongly influences future outcomes. D) The process always reverts back to its mean value.
A) Weibull distribution. B) Exponential distribution. C) Poisson distribution. D) Normal distribution.
A) An equation that calculates the stationary distribution directly. B) An equation that predicts the long-term behavior of the chain. C) An equation that models the uncertainty in transitions. D) An equation that describes the probability of transitioning between states in consecutive time steps.
A) The set of all possible values that the process can take. B) The fixed point of the process. C) The historical record of past observations. D) The set of future predictions.
A) A probability distribution that remains unchanged over time. B) A distribution that depends on the initial state. C) A distribution with constantly changing parameters. D) A distribution that converges to zero over time.
A) Markov process. B) Poisson process. C) Ornstein-Uhlenbeck process. D) Brownian motion.
A) A measure of the periodicity of the process. B) A measure of the linear relationship between values at different time points. C) A measure of the absolute difference between values. D) A measure of the dispersion of values around the mean. |