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Psychological tests often involve item dusters that are designed to solicit responses to behavioral stimuli. The dependency between individual responses within dusters beyond that which can be explained by the underlying trait sometimes reveals structures that are of substantive interest. The paper describes two general classes of models for this type of(More)
Gibbs sampler has been used exclusively for compatible conditionals that converge to a unique invariant joint distribution. However, conditional models are not always compatible. In this paper, a Gibbs sampling-based approach - Gibbs ensemble -is proposed to search for a joint distribution that deviates least from a prescribed set of conditional(More)
In most statistical applications, the Gibbs sampler is the method of choice for inference regarding conditionally specified distributions that are compatible. Compatibility ensures that a unique Gibbs distribution exists. For machine learning of complex models such as dependency networks, the conditional models are sometimes incompatible. In this paper, we(More)
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