# Castedo Ellerman

Proposed answer to the following question(s):

Both variance and entropy are measures of uncertainty. Variance assumes values vary as points in a space with distances between. In this document, the variance of a random vector refers to the variance of the distance from its mean (sum of the variances of each component).

Random one-hot vectors are a convenient spacial representation for categorical random variables. A one-hot vector has all components equal to $$0$$ except one component that equals $$1$$. This representation has been used in genetics . For genetic loci with only two alleles, a one-hot vector has two redundant components. “Half” of such one-hot vectors are typically used in genetics (e.g.  p.40, ,  ). The variance of the “half one-hot vector” is exactly half the variance of its full one-hot vector.

### Main Result

Given $$N$$ independent random one-hot vectors: $$X_1$$, $$X_2$$, …, $$X_N$$ denote $\begin{eqnarray*} X_* = X_1 \times X_2 \times \dots \times X_N \end{eqnarray*}$ as the Cartesian product.

The variance of $$X_*$$ can be adjusted to form a lower bound to the collision entropy, $$\operatorname{H}_2(X_*)$$, and Shannon entropy, $$\operatorname{H}(X_*)$$: $-N \log_2{\left( 1 - \frac{ \operatorname{Var}({ X_*}) }{N} \right)} \; \le \; \operatorname{H}_2(X_*) \; \le \; \operatorname{H}({ X_*})$

If every $$X_i$$ takes only two equally likely values, then the lower bounds reach equality: $-N \log_2{\left( 1 - \frac{ \operatorname{Var}({ X_*}) }{N} \right)} = \operatorname{H}_2({ X_*}) = \operatorname{H}({ X_*}) = N$

### Proof

Let $$M_i$$ be length of $$X_i$$ (the number of categorical values represented by $$X_i$$). Let $$p_{i,j}$$ represent the probability of $$X_i$$ taking the $$j$$-th categorical value.

For every $$1 \le i \le N$$, $\begin{eqnarray*} \sum_{j=1}^{M_i} p_{i,j} = 1 \end{eqnarray*}$

The expectation and variance of the $$i$$-th one-hot vector $$X_i$$ is $\begin{eqnarray*} \operatorname{E}\!\left({ X_i}\right) & = & \left(\; {p}_{i,1} \;,\; {p}_{i,2} \;,\; \dots \;,\; {p}_{i,M_i} \;\right) \\ \operatorname{Var}({ X_i}) & = & \sum_{j=1}^{M_i} p_{i,j} \left[ (1 - p_{i,j})^2 + \sum_{k \not= j} (0 - p_{i,k})^2 \right] \\ & = & \sum_{j=1}^{M_i} p_{i,j} \left[ 1 - 2 p_{i,j} + \sum_{k=1}^{M_i} p_{i,k}^2 \right] \\ & = & 1 - 2 \sum_{j=1}^{M_i} p_{i,j}^2 + \sum_{k=1}^{M_i} p_{i,k}^2 \\ & = & 1 - \sum_{j=1}^{M_i} p_{i,j}^2 \end{eqnarray*}$

Thus the variance of $$X_i$$ equals the probability of two independent samples from $$X_i$$ being distinct. This probability of distinction has been called logical entropy .

The complement $1 - \operatorname{Var}({ X_i}) = \sum_{j=1}^{M_i} p_{i,j}^2$ is the chance of repetition, which is expected probability. Taking the negative log gives Rényi entropy of order 2, also called collision entropy: $-\log_2{( 1 - \operatorname{Var}({ X_i}))} = -\log_2{\left( \sum_{j=1}^{M_i} p_{i,j}^2 \right)} = \operatorname{H}_2(X_i)$ Since negative log is a concave function, the negative log of expected probability (collision entropy), is a lower bound to the expected negative log of probability (Shannon entropy) by Jensen’s inequality: $\operatorname{H}_2(X_i) = -\log_2{\left( \sum_{j=1}^{M_i} p_{i,j}^2 \right)} \le \sum_{j=1}^{M_i} p_{i,j} (-\log_2{p_{i,j}}) = \operatorname{H}({ X_i})$

The total variance, can be adjusted to equal the average probability of one-hot vector repetition (per one-hot vector): $1 - \frac{ \operatorname{Var}({ X_*}) }{N} = 1 - \frac{1}{N} \sum_{i=1}^N \operatorname{Var}({ X_i}) = \frac{1}{N} \sum_{i=1}^N \sum_{j=1}^{M_i} p_{i,j}^2$

Negative log with Jensen’s inequality can then establish yet another lower bound: $-\log_2{\left( \frac{1}{N} \sum_{i=1}^N \sum_{j=1}^{M_i} p_{i,j}^2 \right)} \le \frac{1}{N} \sum_{i=1}^N \left( -\log_2{\sum_{j=1}^{M_i} p_{i,j}^2} \right) = \frac{1}{N} \sum_{i=1}^N \operatorname{H}_2(X_i)$

Collision and Shannon entropy are additive for independent variables. Putting everything together we get: $-N \log_2{\left( 1 - \frac{ \operatorname{Var}({ X_*}) }{N} \right)} \; \le \; \operatorname{H}_2(X_*) \; \le \; \operatorname{H}({ X_*})$

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