stochastic sensitivity analysis

USAGE SUMMARY

The phrase "stochastic sensitivity analysis" is correct and usable in written English.
It can be used in contexts related to statistical modeling, risk assessment, or any field where variability and uncertainty are analyzed in relation to sensitivity. Example: "In our research, we conducted a stochastic sensitivity analysis to determine how changes in input parameters affect the model's output."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

8 human-written examples

Stochastic sensitivity analysis was used to examine the role of antecedent soil water content in the runoff response.

Journal of Hydrology

The sensitivity of different parameters involved in fatigue process pertaining to failure is also studied using stochastic sensitivity analysis.

International Journal of Fatigue

The algorithm generates sample points with Gaussian white noise, and approximates derivatives based on stochastic sensitivity analysis.

Reliability Engineering & System Safety

Stochastic sensitivity analysis was used to analyze the contribution of groups of uncertain inputs to the uncertainty of the N2O emission at point and landscape scale.

Geoderma

In this paper, we propose to augment this generalized probabilistic modeling approach with a stochastic sensitivity analysis in order to quantify and gain insight into separate impacts of distinct sources of uncertainty on quantities of interest.

Reliability Engineering & System Safety

We show that a widely used result of stochastic sensitivity analysis that bolsters this generality is flawed because it ignores the effects of correlations between vital rates.

Duke University

Human-verified similar examples from authoritative sources

Similar Expressions

52 human-written examples

Non-stochastic sensitivity analysis showed results were most sensitive to changes in intervention costs and first-year treatment effect.

Cost Effectiveness and Resource Allocation

The analytical approximations are constructed by combining a mixed linearization approach with a stochastic response sensitivity analysis.

Computer Methods in Applied Mechanics and Engineering

This paper describes a systematic approach that consists of stochastic parameter sensitivity analysis, Bayesian parameter estimation applied to ab initio calculations and experimental data, model-based experimental design, hypothesis mechanism selection, and multistep optimization.

IFAC Proceedings Volumes

Classical uncertainty quantification methods include, but are not limited to, both Frequentist and Bayesian statistical analysis, stochastic models, sensitivity analysis, or Monte Carlo methods [17],[17].

Energy, Sustainability and Society

The sensitivity of CLM-simulated water and energy fluxes to hydrological parameters across 431 MOPEX basins are first examined using an efficient stochastic sampling-based sensitivity analysis approach.

Journal of Hydrology

Expert writing Tips

Best practice

When using "stochastic sensitivity analysis", clearly define the probability distributions of the uncertain inputs to ensure transparency and reproducibility.

Common error

Avoid using "stochastic sensitivity analysis" when a deterministic approach is more appropriate. Ensure that the system being analyzed genuinely involves randomness and that this randomness significantly impacts the outcomes.

Linguistic Context

The phrase "stochastic sensitivity analysis" functions as a technical term describing a method for assessing the impact of uncertainty in model inputs on model outputs. As Ludwig AI notes, it is grammatically correct and usable in English.

Expression frequency: Uncommon

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Academia

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Ludwig's WRAP-UP

In summary, "stochastic sensitivity analysis" is a valid and useful term, as confirmed by Ludwig AI, referring to a method for assessing the impact of uncertain inputs on model outputs. It is most commonly used within the scientific domain. When employing this phrase, it is crucial to define the probability distributions of the uncertain inputs. Related terms include ""probabilistic sensitivity analysis"" and ""uncertainty and sensitivity analysis"". The phrase's frequency is uncommon, highlighting its specialized usage.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

probabilistic sensitivity analysis

This alternative emphasizes the use of probability distributions in the sensitivity analysis.

uncertainty and sensitivity analysis

This alternative broadens the scope to include general uncertainty quantification alongside sensitivity analysis.

global sensitivity analysis under uncertainty

This alternative specifies that the sensitivity analysis is performed considering all possible input variations.

sensitivity analysis with random variables

This alternative highlights the random nature of the variables used in the sensitivity analysis.

statistical sensitivity analysis

This alternative underscores the statistical methods employed in the sensitivity analysis.

risk and sensitivity analysis

This option frames sensitivity analysis within a risk assessment context.

simulation-based sensitivity analysis

The focus is on using simulation techniques to perform sensitivity analysis.

variance-based sensitivity analysis

The alternative focus on the analysis of variance in relation to sensitivity.

parameter sensitivity analysis under uncertainty

The alternative focus on specific parameter variations within the sensitivity analysis.

monte carlo sensitivity analysis

This option is specifically using Monte Carlo simulations as a part of sensitivity analysis.

FAQs

What does "stochastic sensitivity analysis" mean?

"Stochastic sensitivity analysis" is a method to assess how uncertainty in the input of a model affects the uncertainty in the model output. It considers the random nature of the parameters.

When should I use "stochastic sensitivity analysis"?

Use "stochastic sensitivity analysis" when your model includes uncertain parameters represented by probability distributions, and you need to understand how these uncertainties influence the model's results. Consider using "probabilistic sensitivity analysis" as a similar approach.

How is "stochastic sensitivity analysis" different from deterministic sensitivity analysis?

Deterministic sensitivity analysis changes one parameter at a time to see its effect, while "stochastic sensitivity analysis" considers the combined and random effects of multiple uncertain parameters. For less formal scenarios, consider "sensitivity analysis under uncertainty".

What are some techniques used in "stochastic sensitivity analysis"?

Common techniques include Monte Carlo simulation, variance-based methods, and meta-modeling. These techniques help quantify the impact of input uncertainty on model output variability. Another phrase could be "uncertainty and sensitivity analysis".