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The Uncertainty Principle

The Uncertainty Principle

Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...

Uncertainty in Measurement: Reading Instruments

Uncertainty in Measurement: Reading Instruments

Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...

Uncertainty: Overview

Uncertainty: Overview

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.

Uncertainty in Measurement: Significant Figures

Uncertainty in Measurement: Significant Figures

All the digits in a measurement, including the uncertain last digit, are called significant figures or significant digits. Note that zero may be a measured value; for example, if a scale that shows weight to the nearest pound reads “140,” then the 1 (hundreds), 4 (tens), and 0 (ones) are all significant (measured) values.

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

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...

Conditioned Taste Aversion

Conditioned Taste Aversion

Conditioned taste aversion, also known as sauce béarnaise syndrome, is a phenomenon in which an individual develops an aversion to a certain food taste following a negative experience, typically illness. This form of aversion is a type of classical conditioning in which the taste of the food (conditioned stimulus, CS) is associated with the experience of illness (unconditioned stimulus, UCS).
A notable characteristic of conditioned taste aversion is that it often requires only a single...

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Updated: Jan 24, 2026

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Published on: September 16, 2015

在不确定性下对基因电路进行风险不利的优化.

Michal Kobiela¹, Diego A Oyarzún², Michael U Gutmann¹

¹School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK.

|January 22, 2026

概括

此摘要是机器生成的。

这项研究引入了一种结合贝叶斯推理,普森抽样和风险管理的新计算方法,以优化生物电路设计. 这种方法减轻了模型的不准确性,提高了工程生物系统的成功率.

关键词:

普森采样采样普森采样自动化设计自动化设计基因电路的基因电路.机器学习是机器学习.风险偏向优化是风险偏向优化.风险管理风险管理强度坚固性坚固性合成生物学合成生物学不确定性量化不确定性量化

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Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

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科学领域:

合成生物学合成生物学
计算生物学是一种计算生物学.
生物工程是生物工程.

背景情况:

工程生物系统需要导航复杂的设计空间,通常被湿实验室实验所限制.
数学建模和计算优化加速设计,但存在固有的模型不准确性,导致体内性能不足最佳.

研究的目的:

通过解决模型不确定性,开发一个强大的计算框架来设计功能生物电路.
提高工程生物系统的预测准确性和体内性能.

主要方法:

利用贝叶斯推理来估计来自非功能设计的模型参数分布.
采用普森采样和风险偏向优化来选择强大的设计参数.
用不同的模型复杂性和数据类型验证了适应回路和遗传振荡器的方法.

主要成果:

拟议的方法有效地估计参数分布,并确定最佳的,风险不利的设计.
在设计适应电路和遗传振荡器方面,已成功应用.
展示了该方法在各种模型复杂性和数据源中的多功能性.

结论:

贝叶斯推理,普森抽样和风险管理的整合提供了一个强大的策略来降低生物电路设计的风险.
这种计算方法提高了工程功能生物系统的可靠性和效率.
该方法为更可预测和成功的合成生物学应用提供了一条途径.