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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2205.04324 |
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| _version_ | 1866911975408140288 |
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| author | Natarajan, Abhinav Boom, Willem van den Odang, Kristoforus Bryant De Iorio, Maria |
| author_facet | Natarajan, Abhinav Boom, Willem van den Odang, Kristoforus Bryant De Iorio, Maria |
| contents | Gaussian graphical models are useful tools for conditional independence structure inference of multivariate random variables. Unfortunately, Bayesian inference of latent graph structures is challenging due to exponential growth of $\mathcal{G}_n$, the set of all graphs in $n$ vertices. One approach that has been proposed to tackle this problem is to limit search to subsets of $\mathcal{G}_n$. In this paper, we study subsets that are vector subspaces with the cycle space $\mathcal{C}_n$ as main example. We propose a novel prior on $\mathcal{C}_n$ based on linear combinations of cycle basis elements and present its theoretical properties. Using this prior, we implement a Markov chain Monte Carlo algorithm, and show that (i) posterior edge inclusion estimates computed with our technique are comparable to estimates from the standard technique despite searching a smaller graph space, and (ii) the vector space perspective enables straightforward implementation of MCMC algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2205_04324 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | On a wider class of prior distributions for graphical models Natarajan, Abhinav Boom, Willem van den Odang, Kristoforus Bryant De Iorio, Maria Methodology 62H22 (Primary) 05C80, 05C90 (Secondary) Gaussian graphical models are useful tools for conditional independence structure inference of multivariate random variables. Unfortunately, Bayesian inference of latent graph structures is challenging due to exponential growth of $\mathcal{G}_n$, the set of all graphs in $n$ vertices. One approach that has been proposed to tackle this problem is to limit search to subsets of $\mathcal{G}_n$. In this paper, we study subsets that are vector subspaces with the cycle space $\mathcal{C}_n$ as main example. We propose a novel prior on $\mathcal{C}_n$ based on linear combinations of cycle basis elements and present its theoretical properties. Using this prior, we implement a Markov chain Monte Carlo algorithm, and show that (i) posterior edge inclusion estimates computed with our technique are comparable to estimates from the standard technique despite searching a smaller graph space, and (ii) the vector space perspective enables straightforward implementation of MCMC algorithms. |
| title | On a wider class of prior distributions for graphical models |
| topic | Methodology 62H22 (Primary) 05C80, 05C90 (Secondary) |
| url | https://arxiv.org/abs/2205.04324 |