期望算法指定复杂度

BIO-complexity Pub Date : 2019-10-25 DOI:10.5048/bio-c.2019.2

David Nemati, Eric M. Holloway

{"title":"期望算法指定复杂度","authors":"David Nemati, Eric M. Holloway","doi":"10.5048/bio-c.2019.2","DOIUrl":null,"url":null,"abstract":"Algorithmic specified complexity (ASC) is an information metric that measures meaning in an event, based on a chance hypothesis and a context. We prove expectation of ASC with regard to the chance hypothesis is always negative, and empirically apply our finding. We then use this result to prove expected ASC is conserved under stochastic processing, and that complexity for individual events is conserved under deterministic and stochastic processing.","PeriodicalId":89660,"journal":{"name":"BIO-complexity","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Expected Algorithmic Specified Complexity\",\"authors\":\"David Nemati, Eric M. Holloway\",\"doi\":\"10.5048/bio-c.2019.2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Algorithmic specified complexity (ASC) is an information metric that measures meaning in an event, based on a chance hypothesis and a context. We prove expectation of ASC with regard to the chance hypothesis is always negative, and empirically apply our finding. We then use this result to prove expected ASC is conserved under stochastic processing, and that complexity for individual events is conserved under deterministic and stochastic processing.\",\"PeriodicalId\":89660,\"journal\":{\"name\":\"BIO-complexity\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BIO-complexity\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5048/bio-c.2019.2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BIO-complexity","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5048/bio-c.2019.2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 2

摘要

算法指定复杂性（ASC）是一种基于机会假设和上下文来衡量事件意义的信息度量。我们证明了ASC对机会假设的期望总是负的，并实证应用了我们的发现。然后，我们使用这个结果来证明预期ASC在随机处理下是守恒的，并且单个事件的复杂性在确定性和随机处理下也是守恒的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Expected Algorithmic Specified Complexity

Algorithmic specified complexity (ASC) is an information metric that measures meaning in an event, based on a chance hypothesis and a context. We prove expectation of ASC with regard to the chance hypothesis is always negative, and empirically apply our finding. We then use this result to prove expected ASC is conserved under stochastic processing, and that complexity for individual events is conserved under deterministic and stochastic processing.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

BIO-complexity

自引率

0.00%

发文量