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For an example, if we consider weather pattern ( sunny, rainy & cloudy ) then we can say tomorrow’s weather will only depends on today’s weather and not on y’days weather. The most important point Markov Model establishes is that the future state/event depends only on current state/event and not on any other older states (This is known as Markov Property). In Markov Model all the states are visible or observable. Markov Model has been used to model randomly changing systems such as weather patterns. Later using this concept it will be easier to understand HMM.
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The Characteristic Function Property of Mixture Negative Binomial-Exponential Distribution
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The Definite Positive Property of Characteristic Function from Compound Geometric Distribution as The Sum of Gamma Distribution The Property of Continuity And Positively Definite Characteristic Function of Compound Poisson Distribution As The Sum of Geometric Distribution The result has shown for the next one day period the probability of rainfall data available from the three stations will be available following the Viterbi algorithm. The results for the availability of the highest rainfall data for one day ahead is at the Padang Pariaman Climatology Station, with a probability of 0.36, followed by Minangkabau Meteorological Station is 0.35, and Silaing Bawah Geophysics station is 0.29. The results of the prediction show that the Hidden Markov Model can be used to predict the probability of rainfall data availability. This research uses secondary data with a period of one day from the availability of rainfall data at the Minangkabau Meteorological Station, Padang Pariaman Climatology Station, and Silaing Bawah Geophysics Station from January 2018 to July 2019. The purposes of this research are to predict the hidden state of the availability of rainfall data using decoding problems and to find the best state sequence (optimal) by using Viterbi Algorithm, and also to predict probability for the availability of rainfall data in the future by using the Baum Welch Algorithm in the Hidden Markov Model. The availability of rainfall data can form a Markov chain which its state is not able to be observed directly (hidden), is called the Hidden Markov Model (HMM). This position intensely affects the level of rainfall in Indonesia, especially West Sumatra. Indonesia is a maritime continent in Southeast Asian, laying between Indian Ocean and Pacific Ocean.