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Bayesian Approaches to Modeling Interstate Conflict

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Book cover Militarized Conflict Modeling Using Computational Intelligence

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

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Abstract

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.

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  1. University of Johannesburg, Johannesburg, South Africa

    Tshilidzi Marwala & Dr. Monica Lagazio

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  1. Tshilidzi Marwala
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Marwala, T., Lagazio, M. (2011). Bayesian Approaches to Modeling Interstate Conflict. In: Militarized Conflict Modeling Using Computational Intelligence. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-0-85729-790-7_4

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