Timeline for Probability distribution on Python-dictionary-like objects?
Current License: CC BY-SA 4.0
9 events
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| Nov 20 at 10:03 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Oct 21 at 10:58 | comment | added | Lukas | Thanks for the excellent references @BillBradley ! | |
| Oct 21 at 9:38 | history | edited | Lukas | CC BY-SA 4.0 |
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| Oct 10 at 10:30 | comment | added | Bill Bradley | Dear Lukas, this is more for general context than helping with your specific question, but two comments: (1) in terms of "non-constant size" random variables, you may find it interesting to explore non-parametric statistics, and (2) as specific examples where people handle variable-sized clusters of objects, consider checking out the "Chinese Restaurant Process" and "Dirichlet Process Mixture Models". (Of course, these are harder problems, because you're also trying to infer the clusters, but they may have some relevance towards whatever motivates this question.) | |
| Oct 10 at 9:40 | answer | added | Martin Modrák | timeline score: 0 | |
| Oct 8 at 16:53 | comment | added | Iosif Pinelis | I cannot parse your definition of $D(X,K)$. | |
| Oct 8 at 16:32 | comment | added | user76284 | This might be a better way to formulate your question: Let $\mathcal{K}$ be the set of possible keys and $\mathcal{V}$ be the set of possible values. A dictionary is simply an element of $\mathcal{D} = \bigcup_{\mathcal{S} \in \mathcal{P}_\text{finite}(\mathcal{K})} (\mathcal{S} \to \mathcal{V})$, that is, a function from a finite set of keys to values. For any given dictionary $f$, $\operatorname{dom} f$ is its set of keys, and $\operatorname{im} f$ is its set of values. | |
| S Oct 8 at 15:51 | review | First questions | |||
| Oct 8 at 16:00 | |||||
| S Oct 8 at 15:51 | history | asked | Lukas | CC BY-SA 4.0 |