Bayesian Method in Linear Model and Constant Time Series Model Using Non- Informative Prior Under Phenology
DOI:
https://doi.org/10.15415/mjis.2015.32016Keywords:
Bayesian Analysis, Linear Model, Constant Time Series Model, Phenology, Non-Informative PriorAbstract
Climate Change is very recent topic at global level for discussion for all of us. Phenology is one of the main bio- indicators to track climate change effects on ecosystem. The present study is devoted to derive results of coherent interest in the field of phenology from Bayesian point of view. In this paper we have developed the phenological probability models using linear model and constant time series model. The comparison of both the models has also been done using the concept of residual sum of square and Bayes’ factor.
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Berliner, L.M., (2000): Bayesian Climate Change Assesment, Jour. Climate, [3805-3820, 13].
Dose, V. and Menzel, A. (2004): Bayesian Analysis of Climate Change Impacts in Phenology, Global Change Biol., [ 259-272, 17]. http://dx.doi.org/10.1111/j.1529-8817.2003.00731.x
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Menzel, A. (2002): Phenology: Its Importance to the Global Change Community, Jour. Climatic Change [379-385, 54].
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Articles in Mathematical Journal of Interdisciplinary Sciences (Math. J. Interdiscip. Sci.) by Chitkara University Publications are Open Access articles that are published with licensed under a Creative Commons Attribution- CC-BY 4.0 International License. Based on a work at https://mjis.chitkara.edu.in. This license permits one to use, remix, tweak and reproduction in any medium, even commercially provided one give credit for the original creation.
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Mathematical Journal of Interdisciplinary Sciences by Chitkara University Publications is licensed under a Creative Commons Attribution 4.0 International License. Based on a work at https://mjis.chitkara.edu.in |