1,373 research outputs found

    Cutset Sampling for Bayesian Networks

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    The paper presents a new sampling methodology for Bayesian networks that samples only a subset of variables and applies exact inference to the rest. Cutset sampling is a network structure-exploiting application of the Rao-Blackwellisation principle to sampling in Bayesian networks. It improves convergence by exploiting memory-based inference algorithms. It can also be viewed as an anytime approximation of the exact cutset-conditioning algorithm developed by Pearl. Cutset sampling can be implemented efficiently when the sampled variables constitute a loop-cutset of the Bayesian network and, more generally, when the induced width of the networks graph conditioned on the observed sampled variables is bounded by a constant w. We demonstrate empirically the benefit of this scheme on a range of benchmarks

    The fading breadwinner role and the implications for young couples

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    It is a commonplace that the past few decades have been a time of increasing importance in the role of women as income providers, both within and outside of marriage. Drawing on data from the 1964 and 1993 March Current Population Surveys (CPS), we document the changing division of income provision within marriage and the association between changing marital income-provision roles and younger couples' economic welfare over the past thirty years. We find that the proportion of marriages in which husbands are primary breadwinners has declined dramatically, with a corresponding rise in "co-provider" marriages. Regression analyses show that (1) co- provider marriages are economically advantaged compared to other income-provision-role arrangements in both the early 1960s and the early 1990s; and (2) a relatively substantial part of the total improvement in younger couples' economic welfare over time stems from the shift towards co-provider marriages.
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