Multiscale Data Integration Using Markov Random Fields and Markov Chain Monte Carlo: A Field Application in the Middle-East

2003 ◽  
Author(s):  
Adel Malallah ◽  
Hector Perez ◽  
Akhil Datta-Gupta ◽  
Waleed Alamoudi
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Middle East ◽  
Data Integration ◽  
Random Fields ◽  
Markov Random Fields ◽  
Field Application ◽  
Markov Random

Markov Chains in Many Dimensions

1994 ◽  
Vol 26 (3) ◽  
pp. 756-774 ◽  
Author(s):  
Dimitris N. Politis
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Maximum Entropy ◽  
Stationary Distribution ◽  
Random Fields ◽  
Special Class ◽  
Markov Random Fields ◽  
One Dimension ◽  
Stationary Markov Chain ◽  
Markov Random

A generalization of the notion of a stationary Markov chain in more than one dimension is proposed, and is found to be a special class of homogeneous Markov random fields. Stationary Markov chains in many dimensions are shown to possess a maximum entropy property, analogous to the corresponding property for Markov chains in one dimension. In addition, a representation of Markov chains in many dimensions is provided, together with a method for their generation that converges to their stationary distribution.

Modified Markov Chain Monte Carlo Method for Dynamic Data Integration Using Streamline Approach

2008 ◽  
Vol 40 (2) ◽  
pp. 213-232 ◽  
Author(s):  
Yalchin Efendiev ◽  
Akhil Datta-Gupta ◽  
Xianlin Ma ◽  
Bani Mallick
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Method ◽  
Data Integration ◽  
Dynamic Data

scholarly journals An efficient two-stage Markov chain Monte Carlo method for dynamic data integration

2005 ◽  
Vol 41 (12) ◽  
Author(s):  
Y. Efendiev ◽  
A. Datta-Gupta ◽  
V. Ginting ◽  
X. Ma ◽  
B. Mallick
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Method ◽  
Data Integration ◽  
Two Stage ◽  
Dynamic Data
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