Bayesian Approach For Forecasting Model
for crude oil data
(Sprache: Englisch)
Whenever question arises about uncertainty, it can be tackled by Bayesian tools and methods, with the help of priori probabilities and posterior probabilities. In general classical statistics selects just the "best" model and rejects all the others, even of...
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Whenever question arises about uncertainty, it can be tackled by Bayesian tools and methods, with the help of priori probabilities and posterior probabilities. In general classical statistics selects just the "best" model and rejects all the others, even of they are only marginally worse than the best model, perhaps the model is a good fit, but in case of forecasting the future; there it fails. Now, the problem is uncertainty about model, in this book detailed discussion about Bayesian analysis and methods, that in contrast, will combine models of highly comparable for forecasting.
Autoren-Porträt von S. Sam Titus, C. Santharam, Syluvai Anthony
Titus, S. SamS. Sam Titus, obtained his bachelor's degree in Statistics and currently pursuing his post-graduation in Statistics in PG and Research Department of Statistics, Loyola College, Tamil Nadu, India. He stood first in academics and has been awarded Fr. Bertram Medal and many other for his proficiency in Statistics.
Bibliographische Angaben
- Autoren: S. Sam Titus , C. Santharam , Syluvai Anthony
- 2014, 116 Seiten, Maße: 22 cm, Kartoniert (TB), Englisch
- Verlag: LAP Lambert Academic Publishing
- ISBN-10: 3659274763
- ISBN-13: 9783659274763
Sprache:
Englisch
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