A scoring function for learning Bayesian networks based on mutual information and conditional independence tests

We propose a new scoring function for learning Bayesian networks from data using score+search algorithms. This is based on the concept of mutual information and exploits some well-known properties of this measure in a novel way. Essentially, a statistical independence test based on the chi-square distribution, associated with the mutual information measure, together with a property of additive decomposition of this measure, are combined in order to measure the degree of interaction between each variable and its parent variables in the network. The result is a non-Bayesian scoring function called MIT (mutual information tests) which belongs to the family of scores based on information theory. The MIT score also represents a penalization of the Kullback-Leibler divergence between the joint probability distributions associated with a candidate network and with the available data set. Detailed results of a complete experimental evaluation of the proposed scoring function and its comparison with the well-known K2, BDeu and BIC/MDL scores are also presented.I would like to acknowledge support for this work from the Spanish ‘Consejería de Innovación Ciencia y Empresa de la Junta de Andalucía’, under Project TIC-276

Tank gunnery prediction systems

This thesis is concerned with fire control prediction schemes for tanks employed in a defensive role against moving targets. The problem is considered in three parts: the determination of likely target movement patterns in an operational setting; the assessment and modelling of human operator response to those motions; and the utilisation of this response in optimal prediction schemes. In the first part the results from war games, tactical exercises and field trials are collated, and a method is devised for generating test target tracks for human operator study and prediction scheme evaluation. In the second part previous approaches to operator modelling are reviewed, laboratory experiments are described and a mathematical model of human response is developed. In the third part the general statistical properties of predictors are examined, a new class of predictive algorithm called the 'threshold' algorithm is devised, and this type of algorithm is then evaluated using the results of the previous two parts. The thesis ends with some consideration of further research requirements or possibilities, and of the steps needed to validate the results obtained so far.<p