Bayesian Approaches to Modeling Interstate Conflict

Part of the Advanced Information and Knowledge Processing book series (AI&KP)


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.


Simulated Annealing Markov Chain Monte Carlo Receiver Operating Characteristic Curve Gibbs Sampling Gaussian Approximation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.University of JohannesburgJohannesburgSouth Africa

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