LEARNING BAYESIAN NETWORKS FOR REGRESSION FROM INCOMPLETE DATABASES*

In this paper we address the problem of inducing Bayesian network models for regression from incomplete databases. We use mixtures of truncated exponentials (MTEs) to represent the joint distribution in the induced networks. We consider two particular Bayesian network structures, the so-called na¨ıve Bayes and TAN, which have been successfully used as regression models when learning from complete data. We propose an iterative procedure for inducing the models, based on a variation of the data augmentation method in which the missing values of the explanatory variables are filled by simulating from their posterior distributions, while the missing values of the response variable are generated using the conditional expectation of the response given the explanatory variables. We also consider the refinement of the regression models by using variable selection and bias reduction. We illustrate through a set of experiments with various databases the performance of the proposed algorithms

Leveraging Node Attributes for Incomplete Relational Data

Relational data are usually highly incomplete in practice, which inspires us to leverage side information to improve the performance of community detection and link prediction. This paper presents a Bayesian probabilistic approach that incorporates various kinds of node attributes encoded in binary form in relational models with Poisson likelihood. Our method works flexibly with both directed and undirected relational networks. The inference can be done by efficient Gibbs sampling which leverages sparsity of both networks and node attributes. Extensive experiments show that our models achieve the state-of-the-art link prediction results, especially with highly incomplete relational data.Comment: Appearing in ICML 201

The quest for a donor: probability based methods offer help

When a patient in need of a stem cell transplant has no compatible donor within his or her closest family, and no matched unrelated donor can be found, a remaining option is to search within the patient’s extended family. This situation often arises when the patient is of an ethnic minority, originating from a country that lacks a well-developed stem cell donor program, and has HLA haplotypes that are rare in his or her country of residence. Searching within the extended family may be time-consuming and expensive, and tools to calculate the probability of a match within groups of untested relatives would facilitate the search. We present a general approach to calculating the probability of a match in a given relative, or group of relatives, based on the pedigree, and on knowledge of the genotypes of some of the individuals. The method extends previous approaches by allowing the pedigrees to be consanguineous and arbitrarily complex, with deviations from Hardy-Weinberg equilibrium. We show how this extension has a considerable effect on results, in particular for rare haplotypes. The methods are exemplified using freeware programs to solve a case of practical importance

Lee Carter mortality forecasting: application to the Italian population

In this paper we investigate the feasibility of using the Lee-Carter methodology to construct mortality forecasts for the Italian population. We fit the model to the matrix of Italian death rates for each gender from 1950 to 2000. A time-varying index of mortality is forecasted in an ARIMA framework and is used to generate projected life tables. In particular we focus on life expectancies at birth and, for the purpose of comparison, we introduce an alternative approach for forecasting life expectancies on a period basis. The resulting forecasts generated by the two methods are then compared