Application of Bayesian Inference in Time Series Analysis

Krishna Pada Sarkar, K. N. Singh, Achal Lama, Murari Kumar, Bishal Gurung, Rajeev Kumar, , Vinaykumar L. N.
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1<div style="text-align: justify;">ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi-110 012</div>
  • Submitted13-01-2020|

  • Accepted27-12-2019|

  • First Online 27-03-2020|

  • doi 10.18805/BKAP176

Cite article:- Sarkar Pada Krishna, Singh N. K., Lama Achal, Kumar Murari, Gurung Bishal, Kumar Rajeev, N. L. Vinaykumar (2020). Application of Bayesian Inference in Time Series Analysis. Bhartiya Krishi Anusandhan Patrika. 34(3): 225-228. doi: 10.18805/BKAP176.
Nowadays, the availability of huge computing facilities makes it easy to adopt a complex algorithm for various problem-solving. As a result, the Bayesian method of parameter estimation, which is based on Bayes’ theorem given by Thomas Bayes, gains its popularity in recent times. Time series modeling and forecasting is an important aspect of modeling. Model performance and forecasting accuracy can be improved largely using a Bayesian approach. In this article, some basic knowledge about Bayesian inference in time series has been discussed.
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