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Current Issue [Vol. 10, No. 04] [April 2024]
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| Paper Title | :: | Gaussian Mixture of Several Components (Machine Learning) of Daily Ozone and Temperature Maximums in Mexico City Trend 2010-2023 |
| Author Name | :: | M. Sc. Zenteno Jimenez Jose Roberto |
| Country | :: | Mexico |
| Page Number | :: | 01-15 |
In this study we study the trend of maximum Ozone concentrations in Mexico City and Maximum Temperatures, based on the Bivariate Analysis methodology, subsequently using algorithms related to the topic of Data Clustering, especially K-means to be able to observe the classification of groups and subsequently the trend of the different groups of each year of Ozone concentrations and Maximum Temperatures in the City, subsequently we use the Pattern Recognition Gaussian mixture model algorithm.
Keywords: Bivariate Analysis, K-means, Pattern Recognition Gaussian mixture model, Ozone, Maximum Temperatures
Keywords: Bivariate Analysis, K-means, Pattern Recognition Gaussian mixture model, Ozone, Maximum Temperatures
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[2]. Li, Z. Applications of Gaussian Mixture Model to Weather Observations. IEEE Geoscience and Remote Sensing Letters (Volume: 8, Issue: 6, Nov. 2011) McLachlan, G. & Peel, D. Finite Mixture Models.
[3]. Prescott, P., and A. T. Walden, Maximum-likelihood estimation of the parameters of the three-parameter generalized extreme-value distribution from censored samples, J. Stat. Comput. Simul., 6, 241–250,1983.
[4]. Robert, C. P., The Bayesian Choice: A Decision-Theoretic Motivation, Springer Ser. Stat., Springer Verlag, New York, 1994.
[5]. Otten, A., and M. A. J. Van Montfort, Maximum-likelihood estimation of the general extreme-value distribution parameters, J. Hydrol., 47, 187–192, 1980.