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For a random vector $X_{1:n}$,This is equivalent to Han's inequality for relative entropy after expansion against a product reference measure.

For $X^{(i)}=X_{1:n}\setminus X_i$, Han's inequality for relative entropy states that, when $P={⨂}_{i=1}^n{P}_i$,

First prove the chain rule for relative entropy. For two coordinates,Taking expectation under $Q$ givesand iteration proves the chain rule for any finite product.

Write $Q_i(\cdot \mid x_{<i})$ for the successive conditional distributions. Since $P$ is a product, the chain rule givesFor a fixed omitted coordinate $j$, applying the chain rule in the remaining coordinate order givesThe convexity of Kullback-Leibler divergence implies that removing $X_j$ from the conditioning can only decrease each averaged conditional divergence. Summing over $j$, each full conditional increment occurs for exactly the $n-1$ indices $j\ne i$, and thereforewhich is the claimed inequality. Equivalently, this is Han's entropy inequality after expanding each divergence: the product-reference cross-entropy terms cancel because they are modular.

Define the tilted probability measure $Q$ by $dQ=ZdP$; this is normalized because $\mathbb{E}Z=1$. ThenLet ${\mathbb{E}}_i$ average only coordinate $i$, keeping $X^{(i)}$ fixed. The marginal density of $Q_{X^{(i)}}$ relative to $P_{X^{(i)}}$ is ${\mathbb{E}}_{i}Z$, and henceMoreover,Substituting Han's inequality for relative entropy and rearranging gives the tensorization of entropy