Research map / Probabilistic and scientific modeling / Variational and generative sampling
Independence Testing for Bounded Degree Bayesian Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Hellinger-distance independence testing on subsets of d+1 variables, with localization over the bounded-degree network | Degree-d Bayesian networks · Independence testing | sample complexity: 2^{\Omega(n)} -> O(d^2 2^{d / 2} n \log(n) / \varepsilon^2) | Helped |
| Use Hellinger-distance rather than total-variation-distance independence testing | Independence testing for arbitrary distributions over {0,1}^n · {0,1}^n | sample complexity: O(2^{n / 2}/ \varepsilon^4) -> O(2^{n / 2}/ \varepsilon^2) | Helped |
| Allow the Bayesian-network structure to be unknown | Degree-d Bayesian-network independence testing · Independence testing | sample complexity lower bound: \Omega (2^{d / 2} \sqrt{n} / \varepsilon^2) -> \Omega (2^{d / 2} n / \varepsilon^2) | Hurt |
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