Proportional Analogies on Probability Distributions via Bayesian Updating
Quick summary
arXiv:2608.11724v1 Announce Type: new Abstract: Analogies are quaternary relations of the form "A is to B as C is to D". Among the various formalizations of analogical reasoning, proportional analogies provide an important axiomatic framework by characterizing valid analogies through a set of postulates. While proportional analogies have been extensively studied over Boolean, symbolic, and real-valued domains, their extension to probability distributions remains largely unexplored. In this paper, we introduce a notion of proportional analogy for probability distributions based on Bayesian upda
Key takeaways
- arXiv:2608.11724v1 Announce Type: new Abstract: Analogies are quaternary relations of the form "A is to B as C is to D".
- Among the various formalizations of analogical reasoning, proportional analogies provide an important axiomatic framework by characterizing valid analogies through a set of postulates.
- While proportional analogies have been extensively studied over Boolean, symbolic, and real-valued domains, their extension to probability distributions remains largely unexplored.
Why it matters
“Proportional Analogies on Probability Distributions via Bayesian Updating” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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