概率逻辑
计算机科学
编码(社会科学)
软件
软件质量
数据挖掘
口译(哲学)
可靠性(半导体)
二进制数据
二进制数
数据科学
统计
数学
人工智能
软件开发
算术
量子力学
物理
功率(物理)
程序设计语言
作者
John Buckleton,Jo‐Anne Bright,Simone Gittelson,Tamyra R. Moretti,Anthony J. Onorato,Frederick R. Bieber,Bruce Budowle,Duncan Taylor
标识
DOI:10.1111/1556-4029.13898
摘要
Abstract Forensic DNA interpretation is transitioning from manual interpretation based usually on binary decision‐making toward computer‐based systems that model the probability of the profile given different explanations for it, termed probabilistic genotyping ( PG ). Decision‐making by laboratories to implement probability‐based interpretation should be based on scientific principles for validity and information that supports its utility, such as criteria to support admissibility. The principles behind STR mix™ are outlined in this study and include standard mathematics and modeling of peak heights and variability in those heights. All PG methods generate a likelihood ratio ( LR ) and require the formulation of propositions. Principles underpinning formulations of propositions include the identification of reasonably assumed contributors. Substantial data have been produced that support precision, error rate, and reliability of PG , and in particular, STR mix™. A current issue is access to the code and quality processes used while coding. There are substantial data that describe the performance, strengths, and limitations of STR mix™, one of the available PG software.
科研通智能强力驱动
Strongly Powered by AbleSci AI