Bayesian Learning of Measurement and Structural Models

  • Silva R
  • Scheines R
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We present a Bayesian search algorithm for learning the structure
of latent variable models of continuous variables. We stress the
importance of applying search operators designed especially for the
parametric family used in our models. This is performed by searching
for subsets of the observed variables whose covariance matrix can
be represented as a sum of a matrix of low rank and a diagonal matrix
of residuals. The resulting search procedure is relatively efficient,
since the main search operator has a branch factor that grows linearly
with the number of variables. The resulting models are often simpler
and give a better fit than models based on generalizations of factor
analysis or those derived from standard hill-climbing methods.

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  • Ricardo Silva

  • Richard Scheines

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