Weather Patterns The day-to-day changes in weather for a certain part of the country form a Markov process. Each day is sunny, cloudy, or rainy. If it is sunny one day, there is a 70% chance that it will be sunny the following day, a 20% chance that it will be cloudy, and a 10% chance of rain. If it is cloudy one day, there is a 30% chance that it will be sunny the following day, a 50% chance that it will be cloudy, and a 20% chance of rain. If it rains one day, there is a 60% chance that it will be sunny the following day, a 20% chance that it will be cloudy, and a 20% chance of rain. In the long run, what is the daily likelihood of rain?
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- Explain how you can determine the steady state matrix X of an absorbing Markov chain by inspection.arrow_forwardCAPSTONE Explain how to find the nth state matrix of a Markov chain. Explain how to find the steady state matrix of a Markov chain. What is a regular Markov chain? What is an absorbing Markov chain? How is an absorbing Markov chain different than a regular Markov chain?arrow_forwardAbsorbing Markov Chains In Exercises 3740, determine whether the Markov chain with matrix of transition probabilities P is absorbing. Explain. P=[251500153501225151000012]arrow_forward
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- Elementary Linear Algebra (MindTap Course List)AlgebraISBN:9781305658004Author:Ron LarsonPublisher:Cengage Learning