Please use this identifier to cite or link to this item:
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Comparative Study of Heuristic Hybrid of Markov Chain Monte Carlo and Dynamic Programming Methodologies for Network Fault Analysis
Author(s): Jaudet, Mohammad
Iqbal, Naeem
Mirza, Nasir M
Mirza, Sikander M
Hussain, Amir
Contact Email:
Keywords: Data mining
Dynamic programming
Event sequence
Maximum likelihood
Issue Date: Apr-2007
Date Deposited: 27-Aug-2013
Citation: Jaudet M, Iqbal N, Mirza NM, Mirza SM & Hussain A (2007) Comparative Study of Heuristic Hybrid of Markov Chain Monte Carlo and Dynamic Programming Methodologies for Network Fault Analysis. International Journal of Computer Science and Network Security, 7 (4), pp. 32-41.
Abstract: Modeling of network-faults based time-sequence data by piecewise constant intensity function has been carried out using a heuristic approach that employs both Markov Chain Monte Carlo approach (MCMC) and Dynamic Programming algorithm (DPA) methodologies. The results for synthetic as well as for real data show that both MCMC and DPA have close agreement between predicted and actual values. Remarkable speedup (4 to 5 times) has been observed by augmentation of the heuristic method. Due to higher efficiency the proposed approach is well suited for cases with larger data sets requiring near-optimal solution.
Rights: The publisher has not yet responded to our queries therefore this work cannot be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
Licence URL(s):

Files in This Item:
File Description SizeFormat 
Comparative Study of Heuristic Hybrid of Markov Chain Monte.pdfFulltext - Published Version849.8 kBAdobe PDFUnder Permanent Embargo    Request a copy

Note: If any of the files in this item are currently embargoed, you can request a copy directly from the author by clicking the padlock icon above. However, this facility is dependent on the depositor still being contactable at their original email address.

This item is protected by original copyright

Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved

If you believe that any material held in STORRE infringes copyright, please contact providing details and we will remove the Work from public display in STORRE and investigate your claim.