, Volume 63, Issue 3, pp 271–300 | Cite as

A Bayesian approach to nonlinear latent variable models using the Gibbs sampler and the metropolis-hastings algorithm

  • Gerhard Arminger
  • Bengt O. Muthén


Nonlinear latent variable models are specified that include quadratic forms and interactions of latent regressor variables as special cases. To estimate the parameters, the models are put in a Bayesian framework with conjugate priors for the parameters. The posterior distributions of the parameters and the latent variables are estimated using Markov chain Monte Carlo methods such as the Gibbs sampler and the Metropolis-Hastings algorithm. The proposed estimation methods are illustrated by two simulation studies and by the estimation of a non-linear model for the dependence of performance on task complexity and goal specificity using empirical data.

Key words

Gibbs Sampler LISREL model Metropolis-Hastings algorithm Non-linear functions of latent regressors 


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Copyright information

© The Psychometric Society 1998

Authors and Affiliations

  • Gerhard Arminger
    • 1
  • Bengt O. Muthén
    • 2
    • 3
  1. 1.Department of EconomicsFB6, Bergische Universität—GH WuppertalWuppertalGermany
  2. 2.University of CaliforniaLos Angeles
  3. 3.Graduate School of Education & Information StudiesUSA

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