Name
Abstract | ||
|---|---|---|
Inference in Bayesian statistics involves the evaluation of marginal likelihood integrals. We present algebraic algorithms for computing such integrals exactly for discrete data of small sample size. Our methods apply to both uniform priors and Dirichlet priors. The underlying statistical models are mixtures of independent distributions, or, in geometric language, secant varieties of Segre-Veronese varieties. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1577069.1755838 | Journal of Machine Learning Research |
Keywords | Field | DocType |
mixture of indepe ndence model,marginal likelihood integral,exact integration,dirichlet prior,geometric language,computational algebra,independence models,secant variety,marginal likelihood integrals,marginal likelihood,discrete data,bayesian statistic,segre-veronese variety,algebraic algorithm,small sample size,independent distribution,statistical computing,computer algebra,bayesian statistics,statistical model | Pattern recognition,Inference,Marginal likelihood,Algebraic algorithms,Artificial intelligence,Statistical model,Bayesian statistics,Dirichlet distribution,Prior probability,Sample size determination,Mathematics | Journal |
Volume | ISSN | Citations |
10, | 1532-4435 | 2 |
PageRank | References | Authors |
0.63 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shaowei Lin | 1 | 319 | 16.43 |
| Bernd Sturmfels | 2 | 926 | 136.85 |
| Zhiqiang Xu | 3 | 244 | 28.04 |