Opaque prior distributions in Bayesian latent variable models
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866910700188729344 |
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| author | Merkle, Edgar C. Ariyo, Oludare Winter, Sonja D. Garnier-Villarreal, Mauricio |
| author_facet | Merkle, Edgar C. Ariyo, Oludare Winter, Sonja D. Garnier-Villarreal, Mauricio |
| contents | We review common situations in Bayesian latent variable models where the prior distribution that a researcher specifies differs from the prior distribution used during estimation. These situations can arise from the positive definite requirement on correlation matrices, from sign indeterminacy of factor loadings, and from order constraints on threshold parameters. The issue is especially problematic for reproducibility and for model checks that involve prior distributions, including prior predictive assessment and Bayes factors. In these cases, one might be assessing the wrong model, casting doubt on the relevance of the results. The most straightforward solution to the issue sometimes involves use of informative prior distributions. We explore other solutions and make recommendations for practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_08667 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Opaque prior distributions in Bayesian latent variable models Merkle, Edgar C. Ariyo, Oludare Winter, Sonja D. Garnier-Villarreal, Mauricio Methodology We review common situations in Bayesian latent variable models where the prior distribution that a researcher specifies differs from the prior distribution used during estimation. These situations can arise from the positive definite requirement on correlation matrices, from sign indeterminacy of factor loadings, and from order constraints on threshold parameters. The issue is especially problematic for reproducibility and for model checks that involve prior distributions, including prior predictive assessment and Bayes factors. In these cases, one might be assessing the wrong model, casting doubt on the relevance of the results. The most straightforward solution to the issue sometimes involves use of informative prior distributions. We explore other solutions and make recommendations for practice. |
| title | Opaque prior distributions in Bayesian latent variable models |
| topic | Methodology |
| url | https://arxiv.org/abs/2301.08667 |