A Generalized Approach for Bayesian Gaussian Graphical Models Article Swipe
Víthor Rosa Franco
,
Guilherme W. F. Barros
,
Marcos Jiménez
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.31234/osf.io/4s6fh
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.31234/osf.io/4s6fh
Bayesian Gaussian Graphical Models (BGGMs) are tools of growing popularity and interest in network psychometrics and probabilistic graphical modeling. However, some of the existing models are derived from different modeling principles that do not easily allow for extensions and combinations into new models. More specifically, the implementation of some models may not be flexible enough to test different priors or likelihoods. In this paper, we present a new approach to BGGMs that overcomes this limitation by allowing for the estimation of regularized partial correlations between any type of variables while also having an intuistic approach on how to decide about the priors. Our approach is based on using a transformation of the lower diagonal values of the Cholesky (or LDL) decomposition matrix as the parameters of the models, which can receive any zero-centered symmetric distribution as a prior, as well as to include moderators. We have developed the gbggm R package to implement some models based on this approach, and the potentials of the approach are demonstrated with a toy simulation. This new approach expands the range of applications and enhances the flexibility of BGGMs, making them more useful in a variety of contexts.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.31234/osf.io/4s6fh
- https://osf.io/4s6fh/download
- OA Status
- gold
- References
- 87
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4387234129Canonical identifier for this work in OpenAlex
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https://doi.org/10.31234/osf.io/4s6fhDigital Object Identifier
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A Generalized Approach for Bayesian Gaussian Graphical ModelsWork title
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preprintOpenAlex work type
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enPrimary language
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2023Year of publication
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2023-09-30Full publication date if available
- Authors
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Víthor Rosa Franco, Guilherme W. F. Barros, Marcos JiménezList of authors in order
- Landing page
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https://doi.org/10.31234/osf.io/4s6fhPublisher landing page
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goldOpen access status per OpenAlex
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Prior probability, Graphical model, Cholesky decomposition, Computer science, Bayesian probability, Gaussian, Algorithm, Machine learning, Artificial intelligence, Eigenvalues and eigenvectors, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| abstract_inverted_index.which | 128 |
| abstract_inverted_index.while | 89 |
| abstract_inverted_index.BGGMs, | 184 |
| abstract_inverted_index.Models | 3 |
| abstract_inverted_index.decide | 98 |
| abstract_inverted_index.easily | 34 |
| abstract_inverted_index.enough | 54 |
| abstract_inverted_index.having | 91 |
| abstract_inverted_index.making | 185 |
| abstract_inverted_index.matrix | 121 |
| abstract_inverted_index.models | 24, 49, 154 |
| abstract_inverted_index.paper, | 63 |
| abstract_inverted_index.prior, | 137 |
| abstract_inverted_index.priors | 58 |
| abstract_inverted_index.useful | 188 |
| abstract_inverted_index.values | 114 |
| abstract_inverted_index.(BGGMs) | 4 |
| abstract_inverted_index.between | 84 |
| abstract_inverted_index.derived | 26 |
| abstract_inverted_index.expands | 174 |
| abstract_inverted_index.growing | 8 |
| abstract_inverted_index.include | 142 |
| abstract_inverted_index.models, | 127 |
| abstract_inverted_index.models. | 42 |
| abstract_inverted_index.network | 13 |
| abstract_inverted_index.package | 150 |
| abstract_inverted_index.partial | 82 |
| abstract_inverted_index.present | 65 |
| abstract_inverted_index.priors. | 101 |
| abstract_inverted_index.receive | 130 |
| abstract_inverted_index.variety | 191 |
| abstract_inverted_index.Bayesian | 0 |
| abstract_inverted_index.Cholesky | 117 |
| abstract_inverted_index.Gaussian | 1 |
| abstract_inverted_index.However, | 19 |
| abstract_inverted_index.allowing | 76 |
| abstract_inverted_index.approach | 68, 94, 103, 164, 173 |
| abstract_inverted_index.diagonal | 113 |
| abstract_inverted_index.enhances | 180 |
| abstract_inverted_index.existing | 23 |
| abstract_inverted_index.flexible | 53 |
| abstract_inverted_index.interest | 11 |
| abstract_inverted_index.modeling | 29 |
| abstract_inverted_index.Graphical | 2 |
| abstract_inverted_index.approach, | 158 |
| abstract_inverted_index.contexts. | 193 |
| abstract_inverted_index.developed | 146 |
| abstract_inverted_index.different | 28, 57 |
| abstract_inverted_index.graphical | 17 |
| abstract_inverted_index.implement | 152 |
| abstract_inverted_index.intuistic | 93 |
| abstract_inverted_index.modeling. | 18 |
| abstract_inverted_index.overcomes | 72 |
| abstract_inverted_index.symmetric | 133 |
| abstract_inverted_index.variables | 88 |
| abstract_inverted_index.estimation | 79 |
| abstract_inverted_index.extensions | 37 |
| abstract_inverted_index.limitation | 74 |
| abstract_inverted_index.parameters | 124 |
| abstract_inverted_index.popularity | 9 |
| abstract_inverted_index.potentials | 161 |
| abstract_inverted_index.principles | 30 |
| abstract_inverted_index.flexibility | 182 |
| abstract_inverted_index.moderators. | 143 |
| abstract_inverted_index.regularized | 81 |
| abstract_inverted_index.simulation. | 170 |
| abstract_inverted_index.applications | 178 |
| abstract_inverted_index.combinations | 39 |
| abstract_inverted_index.correlations | 83 |
| abstract_inverted_index.demonstrated | 166 |
| abstract_inverted_index.distribution | 134 |
| abstract_inverted_index.likelihoods. | 60 |
| abstract_inverted_index.decomposition | 120 |
| abstract_inverted_index.probabilistic | 16 |
| abstract_inverted_index.psychometrics | 14 |
| abstract_inverted_index.specifically, | 44 |
| abstract_inverted_index.zero-centered | 132 |
| abstract_inverted_index.implementation | 46 |
| abstract_inverted_index.transformation | 109 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5044797587 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I81009624 |
| citation_normalized_percentile.value | 0.17967356 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |