tolerate mistakes
tolerate mistakes
approximate
Monte Carlo method
random-choice algorithm
__BELOW_IS_AI_GENERATED__
間違いを許容する 2023-09-05 01:22 omni.icon
Summary of notes.
The note discusses error-tolerant approaches, approximations, Monte Carlo methods, Miller-Rabin prime determination methods, and random-choice algorithms. These methods seek efficiency and utility at the expense of complete accuracy.
Relation to Fragment.
In the fragment "*1202912207*Python A simple implementation of the Bloom filter" is directly related to the note. The note mentions the Bloom filter, and the fragment describes its concrete implementation; the Bloom filter is an error-tolerant data structure, consistent with the concepts described in the note. deep thinking
Throughout the notes and fragments, the importance of an approach that seeks efficiency and practicality by allowing for a certain amount of error, rather than seeking perfect accuracy, is emphasized. This illustrates that in real-world problem solving, it is often useful to pursue approximate solutions because of the difficulty of finding the perfect solution.
summary of thoughts and title.
Rather than pursuing complete accuracy, an approach that allows for a certain margin of error and pursues efficiency and practicality is more effective in solving real-world problems."
Title: "Error Tolerance Approach and Problem Solving"
extra info
titles: ["Hatena2008-02-13", "Blind cards without pictures yet", "Hatena2008-12-29", "Random but not necessarily uniform distribution", "Geister algorithm explained", "Blind card candidates", "Blind cards", "Simplifying existing games"]
generated: 2023-09-05 01:22
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