From weather conditions to baseball scores to stock performances, many probabilistic real-world systems can be modeled with Markov chains.
In the Markov chain pictured, what is the probability that a process beginning at state A will be at state B after \(2\) moves?
Let \(k\) represent the number of days until a person turns \(200\) years old. What is the biggest problem with this model of life?
Any general stochastic process can be made to satisfy the Markov property by altering the state space (and adding probabilities for any new states). In this way, it can "turn into" a Markov chain. Given a stochastic process with state space \(S\), which of the following methods would create a Markov chain that models the same process?
The Golden State Warriors are strongly affected by morale, and they win games according to the probabilities in the following table:
|States||Win Tomorrow||Lose Tomorrow|
If they lose the first two games in a "best of \(7\)" (first to \(4\)) series, what is the probability that they will be able to come back and win it?