Bayesian approaches to modeling interstate conflict

Tshilidzi Marwala, Monica Lagazio

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Two Bayesian techniques are described in this chapter and compared for interstate conflict prediction. The first one is the Bayesian technique that applies the Gaussian approximation approach to approximate the posterior probability for neural network weights, given the observed data and the evidence framework to train a multi-layer perceptron neural network. The second one treats the posterior probability as is, and then applies the hybrid Monte Carlo technique to train the multi-layer perceptron neural network. When these techniques are applied to model militarized interstate disputes, it is observed that training the neural network with the posterior probability as is, and applying the hybrid Monte Carlo technique gives better results than approximating the posterior probability with a Gaussian approximation method and then applying the evidence framework to train the neural network.

Original languageEnglish
Title of host publicationAdvanced Information and Knowledge Processing
PublisherSpringer London
Number of pages23
Publication statusPublished - 2011

Publication series

NameAdvanced Information and Knowledge Processing
ISSN (Print)1610-3947
ISSN (Electronic)2197-8441

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Information Systems and Management
  • Artificial Intelligence


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