Audio note: this article contains 61 uses of latex notation, so the narration may be difficult to follow. There's a link to the original text in the episode description.

A lot of our work involves "redunds". A random variable <span>_Gamma_</span> is a(n exact) redund over two random variables <span>_X_1, X_2_</span> exactly when both

<span>_X_1 rightarrow X_2 rightarrow Gamma_</span>

<span>_X_2 rightarrow X_1 rightarrow Gamma_</span>

Conceptually, these two diagrams say that <span>_X_1_</span> gives exactly the same information about <span>_Gamma_</span> as all of <span>_X_</span>, and <span>_X_2_</span> gives exactly the same information about <span>_Gamma_</span> as all of <span>_X_</span>; whatever information <span>_X_</span> contains about <span>_Gamma_</span> is redundantly represented in <span>_X_1_</span> and <span>_X_2_</span>. Unpacking the diagrammatic notation and simplifying a little, the diagrams say <span>_P[Gamma|X_1] = P[Gamma|X_2] = P[Gamma|X]_</span> for all <span>_X_</span> such that <span>_P[X] > 0_</span>.