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This application uses a transition matrix to make predictions by using a Markov chain. For exemplification, the values from the transition matrix represent the transition probabilities between two states found in a sequence of observations.

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Gagniuc/Markov-Chains-The-weather

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☁️ Markov Chains The weather

This application uses a 2X2 transition matrix to make predictions by using a Markov chain. For exemplification, the values from the transition matrix represent the transition probabilities between two states found in a sequence of observations (ex. s=RSRRSRRSRRRRSRRRSSSRRSRRRS). These two states are: Sunny and Rainy, or R and S. Based on the initial probability vector, the application calculates how the weather may be on a number of days (steps). Note that a transition matrix can be obtained from a series of observations by using the DPD algorithm. More in-depth information on these matters can be found in the primary source. Note that Markov Chains - The weather is desidned in Visual Basic 6.0 (VB6), thus, the VB6 IDE is needed.

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References

  • Paul A. Gagniuc. Markov chains: from theory to implementation and experimentation. Hoboken, NJ, John Wiley & Sons, USA, 2017, ISBN: 978-1-119-38755-8.

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This application uses a transition matrix to make predictions by using a Markov chain. For exemplification, the values from the transition matrix represent the transition probabilities between two states found in a sequence of observations.

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