Stochastic process

1. A stochastic process is a mathematical object consisting of a collection of random variables, typically indexed by time. It represents the evolution of some system over time where uncertainty or randomness is involved in the system's behavior. Stochastic processes are used in various fields such as finance, physics, biology, and engineering to model random phenomena and analyze their properties. These processes can be classified into different types based on their properties, such as discrete-time or continuous-time, stationary or non-stationary, and Markovian or non-Markovian, providing a powerful framework for studying and understanding complex systems influenced by randomness.

What is a stochastic process?

A) A random process evolving over time.
B) A process that only occurs in discrete steps.
C) A deterministic process with fixed outcomes.
D) A process that remains constant over time.

2. What is the state space of a stochastic process?

A) Set of all possible values that the process can take.
B) Maximum value the process can attain.
C) Average value of the process over time.
D) Exact value of the process at a given time.

3. In a Poisson process, what is the inter-arrival time distribution?

A) Uniform distribution
B) Exponential distribution
C) Normal distribution
D) Bernoulli distribution

4. What is the autocorrelation function of a stochastic process?

A) Measure of correlation between values at different time points.
B) Exact form of the process at a given time.
C) Maximum correlation possible for the process.
D) Average of the process over time.

5. Which of the following is NOT a type of stochastic process?

A) Brownian motion
B) Deterministic process
C) Geometric process
D) Markov process

6. What does ergodicity imply in the context of stochastic processes?

A) Short-term analysis is sufficient for understanding long-term behavior.
B) No inference can be made about long-term behavior.
C) Long-term average behavior can be inferred from a single realization.
D) Behavior is completely random.

7. What is the Law of Large Numbers in the context of stochastic processes?

A) Expected values change with the number of observations.
B) Sample averages diverge from expected values.
C) Randomness decreases with more observations.
D) As the number of observations increases, sample averages converge to expected values.

8. What is the role of a transition matrix in a Markov chain?

A) Calculates the average time spent in each state.
B) Specifies the final state of the process.
C) Describes probabilities of moving to different states.
D) Determines the initial state of the process.

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