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Uncertainty: Overview00:59

Uncertainty: Overview

1.8K
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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Propagation of Uncertainty from Systematic Error01:10

Propagation 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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Propagation of Uncertainty from Random Error00:59

Propagation 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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Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
112.0K
The Uncertainty Principle04:08

The Uncertainty Principle

33.6K
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...
33.6K
Chemical Equilibria: Systematic Approach to Equilibrium Calculations01:21

Chemical Equilibria: Systematic Approach to Equilibrium Calculations

1.8K
Equilibrium calculations for systems involving multiple equilibria are often complex. For example, to calculate the solubility of a sparingly soluble salt in an aqueous solution in the presence of a common ion, one must consider all the equilibria in this solution. Calculations for these systems can be complicated and tedious, so a systematic approach with a series of steps is often helpful. The process is detailed below.
The first step is to identify all the chemical reactions involved, The...
1.8K

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Related Experiment Video

Updated: Feb 27, 2026

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

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Quantifying uncertainty in the chemical master equation.

Basil S Bayati1

  • 1Institute for Disease Modeling, Intellectual Ventures, 3150 139th Ave. SE, Bellevue, Washington 98005, USA.

The Journal of Chemical Physics
|July 3, 2017
PubMed
Summary

This study introduces a new stochastic collocation method to quantify uncertainty in chemical kinetics master equations. The approach effectively analyzes noise effects and shows improved convergence over traditional Monte Carlo simulations for certain model dimensions.

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Area of Science:

  • Computational Chemistry
  • Chemical Kinetics
  • Stochastic Processes

Background:

  • Chemical kinetic master equations are crucial for modeling complex reaction systems.
  • Quantifying uncertainty due to stochasticity (extrinsic and intrinsic noise) is a significant challenge.
  • Existing methods may lack efficiency or accuracy in handling high-dimensional systems.

Purpose of the Study:

  • To develop and present a novel stochastic collocation method for uncertainty quantification in chemical kinetic master equations.
  • To analyze the impact of both extrinsic and intrinsic noise on kinetic models.
  • To compare the proposed method's performance against established techniques like Monte Carlo simulations.

Main Methods:

  • Coupling a stochastic collocation method with an analytical expansion of the master equation.
  • Employing an analytical moment-closure method to derive a system of differential equations with stochastic coefficients.
  • Solving the resulting system using a Smolyak sparse grid collocation method.
  • Applying the method to chemical kinetics problems with time-independent extrinsic noise.

Main Results:

  • The novel method effectively quantifies uncertainty in chemical kinetic master equations with stochastic coefficients.
  • The approach successfully analyzes the effects of extrinsic and intrinsic noise.
  • Demonstrated agreement with classical Monte Carlo simulations.
  • Calculated the variance over time as the sum of two expectations.
  • The method exhibits superior convergence properties for low to moderate dimensions compared to standard Monte Carlo methods.

Conclusions:

  • The presented stochastic collocation method offers a more efficient and accurate alternative for uncertainty quantification in specific regimes of chemical kinetics.
  • This approach provides valuable insights into the role of noise in chemical reaction dynamics.
  • The method's improved convergence makes it a powerful tool for analyzing complex kinetic models.