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Jul 5, 2015
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Abstract

Hidden Markov models and also their own utility in informatics usually are examined. These statistical models are considered as general function string modeling resources that are capable of understanding patterns and also stochastic policies in prediction sequences. The focus with this examines will likely be on general policies with regards to hidden Markov model request. Hidden Markov models tend to be extensions connected with Markov models where by each and every paying attention is actually caused by some sort of stochastic method within on the list of unobserved states. However, popular with a lot of scientists to its one of a kind and also relevant mathematical structure, their independence presumption relating to the consecutive findings hampered even more application. Autoregressive hidden Markov model is actually combining auto-regressive occasion string and also hidden Markov chains. Observations tend to be produced with a number of auto-regressive occasion strings even though the switches involving each and every autoregressive occasion string tend to be manipulated with a hidden Markov chain. With this thesis, we all found the essential aspects, principle and also connected methods and also algorithms intended for hidden Markov models, occasion string and also autoregressive hidden Markov models.

Keywords:  Hidden Markov Model, Viterbi Algorithm, HMM, GMC, Weather

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Author Biography

Manisha, Bhupesh Gaur, TIT Bhopal, MP

CSE Dept.
How to Cite
Bhupesh Gaur, M. (2015). Scientific Software based on Markov Model for Weather Forecast. International Journal of Emerging Trends in Science and Technology, 2(07). Retrieved from http://igmpublication.org/ijetst.in/index.php/ijetst/article/view/780

References

1. Rabiner, L. and Juang, B.H.: Fundamentals of Speech Recognition. Prentice Hall, Upper Saddle River,NJ. (1982) 321-389
2. Devore, Jay: Probability and Statistics for Engineering and the Sciences. Duxbury Pressm, New York, NY. (1995) 154-166
3. Hay L, McCabe J, Wolock D M and Ayers M A 1991 Simulation of precipitation by weathertype analysis; Water Resour. Res. 27 493–501.
4. 13. Hughes J P and Guttorp P A 1994 Class of stochastic models for relating synoptic atmospheric patterns to regional hydrologic phenomena; Water Resour. Res. 30 1535–1546.
5. Poole, David; Mackworth, Alan; Goebel, Randy (1998). Computational Intelligence: A Logical Approach. New York: Oxford University Press. ISBN 0-19-510270-3.
6. Winston, Patrick Henry (1984). Artificial Intelligence. Reading, Massachusetts: Addison-Wesley. ISBN 0-201-08259-4.
7. Rich, Elaine (1983). Artificial Intelligence. McGraw-Hill. ISBN 0-07-052261-8.
8. Rabiner, L. R., A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proceedings of the IEEE, vol. 77, no. 2, Feb. 1989, pgs 257 - 285. There is a lot of notation but verbose explanations accompany.
9. B.Pardo and W. Birmingham. Modeling Form for On-line Following of Musical Performances. AAAI-05 Proc., July 2005.
10. Satish L, Gururaj BI (April 2003). "Use of hidden Markov models for partial discharge pattern classification". IEEE Transactions on Dielectrics and Electrical Insulation.
11. Pr. Pieczynski, W. Pieczynski, Multisensor triplet Markov chains and theory of evidence, International Journal of Approximate Reasoning, Vol. 45, No. 1, pp. 1-16, 2007.
12. Boudaren et al., M. Y. Boudaren, E. Monfrini, W. Pieczynski, and A. Aissani, Dempster-Shafer fusion of multisensor signals in nonstationaryMarkovian context, EURASIP Journal on Advances in Signal Processing, No. 134, 2012.