monte carlo sensitivity analysis

USAGE SUMMARY

The phrase "monte carlo sensitivity analysis" is correct and usable in written English.
It can be used in contexts related to statistical analysis, risk assessment, or financial modeling to describe a method for evaluating the impact of variable changes on outcomes. Example: "In our study, we employed a Monte Carlo sensitivity analysis to assess how variations in input parameters affected the model's predictions."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

5 human-written examples

Monte Carlo sensitivity analysis shows that one station per 2.0 2.5 ha is enough to estimate accurate snow distributions.

Journal of Hydrology

A Monte Carlo sensitivity analysis was carried out for a typical application pattern, considering two different depths of unsaturated limestone (15 and 30 m).

Journal of Contaminant Hydrology

Finally we investigated the relation between number of stations and estimation accuracies of snow distributions using a Monte Carlo sensitivity analysis.

Journal of Hydrology

This paper presents the results of a Monte Carlo sensitivity analysis on the factors (relating to both the building and occupant behaviour) that affect the annual heating energy consumption and the PMV comfort index.

Energy and Buildings

The Monte Carlo sensitivity analysis indicated that the likelihood of CLE being either "dominant" (ie, better clinical outcome with lower cost) or more effective at an acceptable cost was 93% even when all uncertainties associated with the model were taken into account.

Core Evidence

Human-verified similar examples from authoritative sources

Similar Expressions

55 human-written examples

Mahdavi et al., employed sensitivity analysis to better understand stem cell differentiation [18], while Luan et al., used an uncertain mechanistic model of the coagulation cascade in combination with monte-carlo sensitivity analysis, to show that computationally derived sensitive mechanisms were consistent with anticoagulation therapeutic strategies [19].

Plosone

In the presented method, first of all, the matching parameters for regression process are found in an automatic manner by using Monte-Carlo sensitivity analysis.

Fluid Phase Equilibria

Three cell-cycle models were analyzed using monte-carlo sensitivity analysis.

Plosone

In this study, we employ mathematical modeling and monte-carlo sensitivity analysis to explore the working hypothesis that cell-cycle control architectures are HOT networks.

Plosone

The mean and standard deviation obtained from the monte-carlo sensitivity analysis was used to estimate the underlying OSSC distribution (N = 500 points) where the OSSC values were assumed to be normally distributed.

Plosone

When taken together, the top fragile mechanisms for both the G1/S and G2/M phases of the cell-cycle, estimated by monte-carlo sensitivity analysis, were found to be consistent with on-going preclinical and clinical trials for the treatment of a broad spectrum of human cancers.

Plosone

Expert writing Tips

Best practice

When reporting a "monte carlo sensitivity analysis", clearly state the input parameters and their distributions to ensure reproducibility and transparency.

Common error

Avoid using "monte carlo sensitivity analysis" interchangeably with general sensitivity analysis. Monte Carlo specifically involves random sampling to explore the impact of input variations, which may not be present in all sensitivity analyses.

Linguistic Context

The phrase "monte carlo sensitivity analysis" functions as a noun phrase that identifies a specific statistical method. Ludwig AI indicates this phrase is correct and usable in written English, typically within scientific or technical contexts.

Expression frequency: Common

Frequent in

Science

100%

Less common in

News & Media

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Formal & Business

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Ludwig's WRAP-UP

The phrase "monte carlo sensitivity analysis" is a well-established term in statistical modeling, particularly within scientific and technical domains. As Ludwig AI confirms, it is grammatically correct and frequently used. This analysis leverages Monte Carlo simulations to assess the impact of input parameter uncertainties on a model's output, providing a probabilistic understanding of potential outcomes. While alternatives like "probabilistic sensitivity analysis" exist, "monte carlo sensitivity analysis" is specific in its methodology, emphasizing random sampling to explore parameter space.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

probabilistic sensitivity analysis

Focuses on the probabilistic nature of the analysis, emphasizing the uncertainty in the input parameters.

stochastic sensitivity analysis

Highlights the use of stochastic methods, which involve randomness, in the sensitivity analysis process.

uncertainty analysis using monte carlo methods

Emphasizes the assessment of uncertainty and specifies that Monte Carlo methods are employed.

monte carlo simulation for sensitivity

Shifts the focus to Monte Carlo simulation as the primary method for assessing sensitivity.

monte carlo based sensitivity testing

Highlights the testing aspect and the foundation on Monte Carlo methods.

global sensitivity analysis with monte carlo

Specifies that it is a global sensitivity analysis which explores the entire input space, utilizing Monte Carlo.

monte carlo method for parameter sensitivity

Focuses on the application of the Monte Carlo method to assess parameter sensitivity.

simulation based sensitivity analysis

Highlights the use of simulations as a broad approach, where Monte Carlo is a specific type.

quantitative sensitivity assessment

Emphasizes the quantitative nature of the assessment, without specifying the method.

variability analysis using monte carlo

Focuses on analyzing the variability of the results utilizing monte carlo method.

FAQs

What is the purpose of a "monte carlo sensitivity analysis"?

A "monte carlo sensitivity analysis" helps to quantify the uncertainty in a model's output by randomly sampling input parameters from specified distributions and assessing their impact on the model's results. This allows for a better understanding of the range of possible outcomes and the factors driving them.

How does a "monte carlo sensitivity analysis" differ from a standard sensitivity analysis?

While both assess how changes in input parameters affect a model's output, a "monte carlo sensitivity analysis" uses random sampling from probability distributions, providing a more comprehensive exploration of the parameter space than traditional methods that vary parameters one at a time.

When is it appropriate to use a "monte carlo sensitivity analysis"?

It is appropriate when you need to understand the combined effect of multiple uncertain inputs on a model's output, especially when the inputs are correlated or have non-linear relationships with the output. It's also useful when a probabilistic assessment of the results is required.

What are some alternatives to "monte carlo sensitivity analysis"?

Alternatives include "probabilistic sensitivity analysis", "stochastic sensitivity analysis", and variance-based sensitivity analysis. The choice depends on the specific goals of the analysis and the nature of the model.