Bayesian Network Structure Learning with Permutation Tests

In literature there are several studies on the performance of Bayesian network structure learning algorithms. The focus of these studies is almost always the heuristics the learning algorithms are based on, i.e. the maximisation algorithms (in score-based algorithms) or the techniques for learning the dependencies of each variable (in constraint-based algorithms). In this paper we investigate how the use of permutation tests instead of parametric ones affects the performance of Bayesian network structure learning from discrete data. Shrinkage tests are also covered to provide a broad overview of the techniques developed in current literature.Comment: 13 pages, 4 figures. Presented at the Conference 'Statistics for Complex Problems', Padova, June 15, 201

On using Bayesian networks for complexity reduction in decision trees

Bayesian networks, Decision trees, Markov blanket, Complexity reduction, Classification,

Apprendimento di modelli grafici esplorativi per la valutazione in ambito socio-sanitario: il caso dell'assistenza informale

The present work is part of a wider project of work and study intended to research and describe variables, indicators, and their relationships associated with the experience of families which, together or through one of their members, live through exceptional experiences, such as for example hospitalisation, or much more commonly and on a more daily level, access to the most elementary social and health services. In particular the level of satisfaction perceived from hospital use with regard to care received will be explored and described. Naturally such events take on different characteristics depending on the demographic, epidemiological, social and economic typology of the family involved. The database used in the work project is the one obtained in the sample surveys which ISTAT (Central Institute of Statistics) organised in Italy on health conditions and the use made of social and health services by Italian families. Since 1980 ISTAT has organised and carried out these surveys through families in order to learn about health conditions, use of social and health services, and some habits or lifestyles including risk factors(senza \u2018s (risk\u2019s factors) (smoking, alcohol, etc.). These surveys had at most a three-year time limit and are characterised by the fact that they were carried out over the whole national territory, each time involving more than 23,000 families and over 93,000 people (ISTAT\u2026.). For the present analysis the data were selected from the Multipurpose Survey on Families carried out by ISTAT in 1998 called \u201cFamilies, social subjects and infancy conditions\u201d. In the papaer we concentrate on Bayesian approach where our interest is turned in the construction of the tipology of network(learning9direct towardsthe complex system. operationally the problem was to offer the most probable (MAP) model from a complete database in the context of bayesian Network

On using Bayesian networks for complexity reduction in decision trees

Bayesian networks, Decision trees, Markov blanket, Complexity reduction, Classification,