This paper describes a procedure for learning the structure of latent variable models with the primary purpose of performing density estimation in continu- ous ...

We present a Bayesian search algorithm for learning the structure of latent variable models of continuous variables. We stress the importance of applying ...

We present a Bayesian search algorithm for learning the structure of latent variable mod- els of continuous variables. We stress the.

With an informative prior distribution over the parameters, it can be used to estimate underidentified models, as we illustrate on a simple errors-in-variables.

A Bayesian approach to model selection for structural equation models is outlined. This enables us to compare individual models, nested or non-nested, ...

The distinguishing feature of Bayesian inference is the specification of the prior distribution for the model parameters. The difficulty arises in how a ...

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Bayesian combines prior distributions with the data likelihood to form posterior distributions to estimate the parameters. An algorithm based on the Gibbs ...

Oct 10, 2024 · This paper explores the utilisation of Bayesian structural equation modelling (BSEM) in psychology, highlighting its advantages over frequentist methods.

This article introduces a Bayesian approach in analyzing structure equation models. This approach can utilize prior information for achieving better ...