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# Experiments in entropy as a functor

Yesterday I wrote a blog article on categories that show up in probability theory, as a kind of warmup for finishing off a paper with Tobias Fritz.

This made me want to organize some old notes on this topic. So, I just took some material from the nLab and made it into an Azimuth page:

I have mixed feelings about this material. On the one hand it's rather esoteric and doesn't yet have a "killer app" to justify it. On the other hand, it brings entropy into contact with mathematical ideas that pure mathematicians like, which should someday be useful. And it's cute.

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1.
edited June 2013

In this part:

We begin with a sadly familiar problem:

Suppose you live in a town with a limited number of tolerable restaurants. Every Friday you go out for dinner. You randomly choose a restaurant according to a certain probability distribution $P$. If you go to the $i$th restaurant, you then choose a dish from the menu according to some probability distribution $Q_i$. How surprising will your choice be, on average?

[...]

Glomming together probabilities

But the interesting thing about this problem is that it involves an operation which I'll call 'glomming together' probability distributions. First you choose a restaurant according to some probability distribution $P$ on the set of restaurants. Then you choose a meal according to some probability distribution $Q_i$. If there are $n$ restaurants in town, you wind up eating meals in a way described by some probability distribution we'll call

$$P \circ (Q_1, \dots, Q_n )$$ A bit more formally:

Suppose $P$ is a probability distribution on the set $\{1,\dots, n\}$ and $Q_i$ are probability distributions on finite sets $X_i$, where $i = 1, \dots, n$. Suppose the probability distribution $P$ assigns a probability $p_i$ to each element $i \in \{1,\dots, n\}$, and suppose the distribution $Q_i$ assigns a probability $q_{i j}$ to each element $j \in X_i$.

I want to extend the $Q_i$ to all the $X_k$ with zeroes when $k \neq i$, so that each $Q_i$ is a distribution over the same set $U$, the disjoint union of the $X_i$, and $j$ runs over $U$ in the definition of the $q_{ij}$. Then what you call glomming is a mixture of distributions. People often use mixtures, but I've never heard of glomming!

The formula with the extra term reminds of the law of total variance', see http://statisticalmodeling.wordpress.com/2011/06/16/the-variance-of-a-mixture/.

Comment Source:In this part: > We begin with a sadly familiar problem: > Suppose you live in a town with a limited number of tolerable restaurants. Every Friday you go out for dinner. You randomly choose a restaurant according to a certain probability distribution $P$. If you go to the $i$th restaurant, you then choose a dish from the menu according to some probability distribution $Q_i$. <i>How surprising will your choice be, on average?</i> [...] > Glomming together probabilities > But the interesting thing about this problem is that it involves an operation which I'll call 'glomming together' probability distributions. First you choose a restaurant according to some probability distribution $P$ on the set of restaurants. Then you choose a meal according to some probability distribution $Q_i$. If there are $n$ restaurants in town, you wind up eating meals in a way described by some probability distribution we'll call > $$P \circ (Q_1, \dots, Q_n )$$ > A bit more formally: > Suppose $P$ is a probability distribution on the set $\{1,\dots, n\}$ and $Q_i$ are probability distributions on finite sets $X_i$, where $i = 1, \dots, n$. Suppose the probability distribution $P$ assigns a probability $p_i$ to each element $i \in \{1,\dots, n\}$, and suppose the distribution $Q_i$ assigns a probability $q_{i j}$ to each element $j \in X_i$. I want to extend the $Q_i$ to all the $X_k$ with zeroes when $k \neq i$, so that each $Q_i$ is a distribution over the same set $U$, the disjoint union of the $X_i$, and $j$ runs over $U$ in the definition of the $q_{ij}$. Then what you call glomming is a mixture of distributions. People often use mixtures, but I've never heard of glomming! The formula with the extra term reminds of the law of total variance', see [http://statisticalmodeling.wordpress.com/2011/06/16/the-variance-of-a-mixture/](http://statisticalmodeling.wordpress.com/2011/06/16/the-variance-of-a-mixture/).
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"Glom" is just American slang for "stick together", not a technical term.

You're right that "glomming" is a special case of a mixture of distributions. But I suspect that the rather simple formula for the entropy of a probability distribution formed by 'glomming' gets more complicated for mixtures of probability distributions that don't have disjoint supports.

Yes, that formula for the variance of a mixture is somehow related! I'm not sure exactly how to fit them in a common framework....

Comment Source:"Glom" is just American slang for "stick together", not a technical term. You're right that "glomming" is a special case of a mixture of distributions. But I suspect that the rather simple formula for the entropy of a probability distribution formed by 'glomming' gets more complicated for mixtures of probability distributions that don't have disjoint supports. Yes, that formula for the variance of a mixture is somehow related! I'm not sure exactly how to fit them in a common framework....
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I think it's very useful to have gathering this material on a page. Some I'd missed and others I'd certainly like to reread. Whether Zurek's ideas and non-extensive entropies in any shape have any usable connections to the kinds of models Azimuth people want seems like a very good question.

Comment Source:I think it's very useful to have gathering this material on a page. Some I'd missed and others I'd certainly like to reread. Whether Zurek's ideas and non-extensive entropies in any shape have any usable connections to the kinds of models Azimuth people want seems like a very good question.