Principled selection of impure measures for consistent learning of linear latent variable models
نویسنده
چکیده
In previous work, we have developed a principled way of learning the causal structure of linear latent variable models (Silva et al., 2006). However, we have considered the case for models with pure measures only. Pure measures are observed variables that measure no more than one latent variable. This paper presents theoretical extensions that justify the selection of some types of impure measures, allowing us to discover hidden variables that could not be identified in the previous case.
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