Review:
Markov Processes
overall review score: 4.5
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score is between 0 and 5
Markov processes are stochastic processes that involve a sequence of events in which the probability of each event depends only on the state attained in the previous event.
Key Features
- Memoryless property
- Transition probabilities
- State space
- Markov property
Pros
- Useful for modeling real-world systems with probabilistic transitions
- Widely used in various fields such as physics, biology, finance, and computer science
Cons
- Can be complex to analyze and solve for specific scenarios
- Requires understanding of probability theory and linear algebra