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In this paper, we discuss efforts to apply a novel Bayesian network (BN) structure learning algorithm to a real world epidemiological problem, namely the Nasopharyngeal Carcinoma (NPC). Our specific aims are : (1) to provide a statistical profile of the recruited population, (2) to help indentify the important environmental risk factors involved in NPC, and (3) to gain insight on the applicability and limitations of BN methods on small epidemiological data sets obtained from questionnaires. We discuss first the novel BN structure learning algorithm called Max-Min Parents and Children Skeleton (MMPC) developped by Tsamardinos et al. in 2005. MMPC was proved by extensive empirical simulations to be an excellent trade-off between time and quality of reconstruction compared to most constraint based algorithms, especially for the smaller sample sizes. Unfortunately, MMPC is unable to deal with datasets containing approximate functional dependencies between variables. In this work, we overcome this problem and apply the new version of MMPC on Nasopharyngeal Carcinoma data in order to shed some light into the statistical profile of the population under study.