Conditional distributions for the nested Dirichlet process via sequential imputation
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915541960097792 |
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| author | Donald, Evan Swanson, Jason |
| author_facet | Donald, Evan Swanson, Jason |
| contents | We consider an array of random variables, taking values in a complete and separable metric space, that exhibits a kind of symmetry which we call row exchangeability. Given such an array, a natural model for Bayesian nonparametric inference is the nested Dirichlet process (NDP). Exactly determining posterior distributions for the NDP is infeasible, since the computations involved grow exponentially with the sample size. In this paper, we present a new approach to determining these posterior distributions that involves the use of sequential |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_00451 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Conditional distributions for the nested Dirichlet process via sequential imputation Donald, Evan Swanson, Jason Statistics Theory Probability 62G05 (Primary) 60G57, 62D10, 62M20 (Secondary) We consider an array of random variables, taking values in a complete and separable metric space, that exhibits a kind of symmetry which we call row exchangeability. Given such an array, a natural model for Bayesian nonparametric inference is the nested Dirichlet process (NDP). Exactly determining posterior distributions for the NDP is infeasible, since the computations involved grow exponentially with the sample size. In this paper, we present a new approach to determining these posterior distributions that involves the use of sequential |
| title | Conditional distributions for the nested Dirichlet process via sequential imputation |
| topic | Statistics Theory Probability 62G05 (Primary) 60G57, 62D10, 62M20 (Secondary) |
| url | https://arxiv.org/abs/2505.00451 |