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Gaussian Process Structural Equation Models with Latent Variables

Authors
  • Silva, Ricardo
  • Gramacy, Robert B.
Type
Preprint
Publication Date
Mar 12, 2010
Submission Date
Feb 25, 2010
Identifiers
arXiv ID: 1002.4802
Source
arXiv
License
Yellow
External links

Abstract

In a variety of disciplines such as social sciences, psychology, medicine and economics, the recorded data are considered to be noisy measurements of latent variables connected by some causal structure. This corresponds to a family of graphical models known as the structural equation model with latent variables. While linear non-Gaussian variants have been well-studied, inference in nonparametric structural equation models is still underdeveloped. We introduce a sparse Gaussian process parameterization that defines a non-linear structure connecting latent variables, unlike common formulations of Gaussian process latent variable models. The sparse parameterization is given a full Bayesian treatment without compromising Markov chain Monte Carlo efficiency. We compare the stability of the sampling procedure and the predictive ability of the model against the current practice.

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