相关概念视频
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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00:59Uncertainty: Overview
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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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00:59Propagation of Uncertainty from Random Error
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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01:10Propagation of Uncertainty from Systematic Error
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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00:54Uncertainty: Confidence Intervals
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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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在不确定性下基于代理的模型.
Vladimir Stepanov1, Scott Ferson1
1Institute for Risk and Uncertainty, University of Liverpool, Liverpool, England, L69 7ZX, UK.
F1000Research
|April 4, 2024
概括
蒙特卡洛模拟不适合在基于代理的模型 (ABM) 中的认识不确定性. 间隔实施提供了广泛的系统界限,但缺乏对预期结果的洞察力.
科学领域:
- 计算科学 计算科学
- 复杂系统建模 复杂系统建模
背景情况:
- 基于代理的模型 (ABM) 经常使用蒙特卡洛 (MC) 模拟来评估不确定性.
- MC 适用于 aleatory 不确定性 (变化),但不适用于 epistemic 不确定性 (缺乏知识).
- 这项研究在基于代理的战舰模拟中模拟了认识体系的不确定性.
研究的目的:
- 在基于代理的模型 (ABM) 中对比蒙特卡洛 (MC) 和区间实现的认识体系不确定性.
- 在复杂的模拟中评估不同不确定性量化方法的适用性.
- 分析不完善信息 (如雷达) 对模拟结果的影响.
主要方法:
- 开发了一个战舰模拟器,其中代理人代表船只.
- 实施了蒙特卡洛 (MC) 和基于区间的方法来模型认识不确定性.
- 引入了一个不完美的雷达系统来模拟对代理人身份缺乏知识.
主要成果:
- 间隔实施提供了广泛的系统界限,但缺乏对预期结果的定量洞察力.
- 与间隔方法相比,MC模拟往往以较少的剩余剂得出结论.
- 间隔方法中幸存剂的身份与MC结果部分重叠,但总身份较少.
结论:
- 间隔方法可以在ABM中实现,产生用于定义系统界限的有用结果.
- 间隔方法不能提供对不确定的环境中预期结果或趋势的清晰洞察力.
- 选择不确定性量化方法对模拟解释和决策产生重大影响.