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相关概念视频

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
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Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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Design Consideration01:22

Design Consideration

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Designing a structure involves a series of considerations, primarily the material's ultimate strength, calculated through tests that measure changes under increased force until the material reaches its breaking point or limit. The ultimate load, where the material breaks, is divided by its original cross-sectional area, resulting in the ultimate normal stress or strength. The ultimate shearing stress is another significant factor taken into account.
The factor of safety is another key...
179
Statistical Analysis System (SAS)01:14

Statistical Analysis System (SAS)

102
SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...
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Relative Risk01:12

Relative Risk

113
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
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相关实验视频

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An R-Based Landscape Validation of a Competing Risk Model
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一个对抗性风险分析框架用于软件发布决策支持.

Refik Soyer1, Fabrizio Ruggeri2, David Rios Insua3

  • 1Department of Decision Sciences, George Washington University, Washington, District of Columbia, USA.

Risk analysis : an official publication of the Society for Risk Analysis
|February 6, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一个新的AI风险分析框架,以帮助开发人员决定何时发布人工智能 (AI) 产品,考虑可靠性,成本和竞争.

关键词:
对抗风险分析对抗风险分析人工智能的人工智能是人工智能.风险分析 风险分析软件工程 软件工程 软件工程战略分析 战略分析

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相关实验视频

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科学领域:

  • 软件工程 软件工程 软件工程
  • 人工智能风险管理 人工智能风险管理
  • 决策分析 决策分析

背景情况:

  • 严格的质量约束和竞争激烈的市场需要谨慎的AI系统发布决策.
  • 人工智能系统的可靠性,安全性,故障分析,成本和竞争对手的行为使发布时间变得复杂.
  • 当前的人工智能风险管理框架需要强大的方法来做出市场发布决策.

研究的目的:

  • 为AI系统发布决策提出一个新的一般对抗风险分析框架.
  • 支持人工智能开发人员应对进入市场的复杂性.
  • 在不确定性下提供一个结构化的方法来评估AI产品发布.

主要方法:

  • 开发一个多代理对抗风险分析框架.
  • 在框架内,将生产者和买家作为不同的代理类型进行建模.
  • 用实例说明框架的实施情况.

主要成果:

  • 拟议的框架为人工智能开发人员提供了一种评估发布时间的方法.
  • 该框架考虑了生产者和买家之间的敌对互动.
  • 讨论了多个生产者和买家的扩展,提高了适用性.

结论:

  • 新的对抗风险分析框架为AI发布决策提供了宝贵的工具.
  • 解决不确定性和竞争动态对于成功部署人工智能产品至关重要.
  • 该框架可以扩展到涉及多个利益相关方的更复杂的市场场景.