Square Hellinger Subadditivity for Bayesian Networks and its Applications to Identity Testing


Constantinos Daskalakis, Qinxuan Pan ;
Proceedings of the 2017 Conference on Learning Theory, PMLR 65:697-703, 2017.


We show that the square Hellinger distance between two Bayesian networks on the same directed graph, $G$, is subadditive with respect to the neighborhoods of $G$. Namely, if $P$ and $Q$ are the probability distributions defined by two Bayesian networks on the same DAG, our inequality states that the square Hellinger distance, $H^2(P,Q)$, between $P$ and $Q$ is upper bounded by the sum, $\sum_v H^2(P_{v} ∪\Pi_v, Q_{v} ∪\Pi_v)$, of the square Hellinger distances between the marginals of $P$ and $Q$ on every node $v$ and its parents $\Pi_v$ in the DAG. Importantly, our bound does not involve the conditionals but the marginals of $P$ and $Q$. We derive a similar inequality for more general Markov Random Fields. As an application of our inequality, we show that distinguishing whether two (unknown) Bayesian networks $P$ and $Q$ on the same (but potentially unknown) DAG satisfy $P=Q$ vs $d_\rm TV(P,Q)>ε$ can be performed from $\tilde{O}(|Σ|^3/4(d+1) ⋅n/ε^2)$ samples, where $d$ is the maximum in-degree of the DAG and $Σ$ the domain of each variable of the Bayesian networks. If $P$ and $Q$ are defined on potentially different and potentially unknown trees, the sample complexity becomes $\tilde{O}(|Σ|^4.5 n/ε^2)$. In both cases the dependence of the sample complexity on $n, ε$ is optimal up to logarithmic factors. Lastly, if $P$ and $Q$ are product distributions over ${0,1}^n$ and $Q$ is known, the sample complexity becomes $O(\sqrt{n}/ε^2)$, which is optimal up to constant factors.

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