Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

Search 250 million research papers

Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.

About 10,000 papers for “Random variable”

Random variable generators

Robert E. Wheeler · ACM SIGSIM Simulation Digest · 1976

article Free Access Share on Random variable generators Author: Robert E. Wheeler E. I. du Pont de Nemours & Company, Inc., Wilmington, Deleware E. I. du Pont de Nemours & Company, Inc., Wilmington, DelewareView Profile Authors Info & Claims ACM SIGSIM Simulation DigestVolume 8Issue 1October 1976 pp 16–23https://doi.org/10.1145/1103189.1103194Online:01 October 1976Publication History 0citation59DownloadsMetricsTotal Citations0Total Downloads59Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Cited by 0Open access

The Random Variable

X. Rong Li · 2017

This chapter deals with the random variable (RV) approach, a powerful and effective tool for many random problems. It is utilizes every outcome of a random experiment as a unique number and thus makes many powerful tools in calculus applicable. The chapter covers the concept of random variable, cumulative distribution function, probability density function, and uniform distribution. It also discusses Gaussian distribution, expectation and moments, functions of a random variable and generation of random numbers. A random variable is simply a dependent variable as a function of an independent variable – the outcomes of a random experiment. It is a numerical description of the outcomes. RVs may be grouped into three types, depending on the range of the function: a discrete RV is one that may take on only discrete values; a continuous RV is one having a continuous range of values; and a mixed RV is one with both discrete and continuous values.

Cited by 0

Variable range random walk

Takashi Odagaki · arXiv (Cornell University) · 2021

Exploiting the coherent medium approximation, random walk among sites distributed randomly in space is investigated when the jump rate depends on the distance between two adjacent sites. In one dimension, it is shown that when the jump rate decays exponentially in the long distance limit, a non-diffusive to diffusive transition occurs as the density of sites is increased. In three dimensions, the transition exists when the jump rate has a super Gaussian decay.

Cited by 0Open access

Random forest variable importance.

Kwang-Sig Lee (7587932), In-Seok Song (17720212), Eun Sun Kim (843999) et al. · Figshare · 2024

<div><p>This study employs machine learning analysis with population data for the associations of preterm birth (PTB) with temporomandibular disorder (TMD) and gastrointestinal diseases. The source of the population-based retrospective cohort was Korea National Health Insurance claims for 489,893 primiparous women with delivery at the age of 25–40 in 2017. The dependent variable was PTB in 2017. Twenty-one predictors were included, i.e., demographic, socioeconomic, disease and medication information during 2002–2016. Random forest variable importance was derived for finding important predictors of PTB and evaluating its associations with the predictors including TMD and gastroesophageal reflux disease (GERD). Shapley Additive Explanation (SHAP) values were calculated to analyze the directions of these associations. The random forest with oversampling registered a much higher area under the receiver-operating-characteristic curve compared to logistic regression with oversampling, i.e., 79.3% vs. 53.1%. According to random forest variable importance values and rankings, PTB has strong associations with low socioeconomic status, GERD, age, infertility, irritable bowel syndrome, diabetes, TMD, salivary gland disease, hypertension, tricyclic antidepressant and benzodiazepine. In terms of max SHAP values, these associations were positive, e.g., low socioeconomic status (0.29), age (0.21), GERD (0.27) and TMD (0.23). The inclusion of low socioeconomic status, age, GERD or TMD into the random forest will increase the probability of PTB by 0.29, 0.21, 0.27 or 0.23. A cutting-edge approach of explainable artificial intelligence highlights the strong associations of preterm birth with temporomandibular disorder, gastrointestinal diseases and antidepressant medication. Close surveillance is needed for pregnant women regarding these multiple risks at the same time.</p></div>

Cited by 0Open access

Random Variable Practice problems

Bulletin of Miscellaneous Information (Royal Gardens Kew)

This is a set of Random variable practice problems I made up for my students in probability and Stats class. (Gaussian, Poisson, Binomial)

Cited by 0

Random matrix variable prioritization.

Shadrack Kipkogei (20513684), Jiqin Han (20457821), Gershom Mwalupaso (20513687) et al. · Figshare · 2025

<div><p>Addressing global food security demands urgent improvement in agricultural productivity, particularly in developing economies where market imperfections are perverse and resource constraints prevail. While microcredit is widely acknowledged as a tool for economic empowerment, its role in facilitating agricultural technology adoption and improving agricultural incomes remains underexplored. This study examines the synergistic effects of microcredit access and agricultural technology adoption on the incomes of maize farmers in Kenya. Using household-level data, we employ an endogenous switching regression framework to control possible endogeneity in access to microcredit. Our findings shows that microcredit access positively influences the adoption of advanced agricultural technologies. Key determinants, including marital status, use of fertilizer application, access to extension services, and cooperative membership, are identified as significant determinants of microcredit access. Notably, the Average Treatment Effect on the Treated (ATT) indicates a 40.52% increase in income among farmers who access microcredit, mainly driven by the timely adoption of high-quality seeds, improved agricultural technologies, and enhanced inputs. These results highlight microcredit’s role in promoting allocative efficiency and enhancing Total Factor Productivity (TFP) within agricultural systems. Robustness checks, including propensity score matching and sensitivity analyses, corroborate these findings. The study recommends the implementation of targeted financial policies and educational initiatives meant to promote credit access, encourage savings, and enhancing financial literacy, particularly for credit-constrained households. Integrating these measures could strengthen rural financial markets and drive sustainable agricultural development across the regions.</p></div>

Cited by 0Open access

THE PYTHAGOREAN RANDOM VARIABLE

Gary Allan Heckmann · UA Campus Repository (The University of Arizona) · 2016

Rights Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.

Cited by 0

Random forest variable importance

Breavington, Jessica · Figshare · 2024

Two random forest algorithms to train and test feature importance of temporal, environmental, and soil physiochemical properties on CO<sub>2</sub><sub> </sub>flux from mangrove soils in the Red Sea under 12 hours of incubation in light conditions and, separately, under 12 hours of incubation in dark conditions. Soil properties were measured with separate soil cores collected immediately adjacent to the incubated cores. 4 replicate cores were collected each month and CO<sub>2</sub> flux measured by G2201-i CRDS analyser (Picarro, Santa Clara, California USA). The random forest models were built in Python v.3.9.13 and Jupyter Notebook v.6.4.12 using the RandomForestRegressor from the SciKit-Learn package v.1.0.2. Using the datasets uploaded, 80% of data was randomly selected and used for training, with the remaining 20% used for validation. To improve model performance, hyperparameter tuning was used to maximize the R<sup>2</sup> metric and 5-fold cross-validation used to assess how the model generalizes to unseen data and reduces the risk of overfitting. Subsequently, a baseline accuracy threshold was defined for feature selection, where R<sup>2</sup> ≥ 0.6 and the average 5-fold cross-validation (CV) score ≥ 0.4. Backward elimination of variables based on these performance metrics was systematically performed to maximise the number of variables included within each model without sacrificing model performance to ensure the maximum predictive power running ~120,000 model iterations. These models were used to map feature importance of the variables retained from the feature selection stage.These models were used to present the results in: J Breavington, A Steckbauer, C Fu, M Ennasri, and CM. Duarte. 'Dynamics of CO2 and CH4 fluxes in Red Sea mangrove soils'.

Cited by 0Open access

Random forest variable importance

Breavington, Jessica · Figshare · 2024

Two random forest algorithms to train and test feature importance of temporal, environmental, and soil physiochemical properties on CO<sub>2</sub><sub> </sub>flux from mangrove soils in the Red Sea under 12 hours of incubation in light conditions and, separately, under 12 hours of incubation in dark conditions. Soil properties were measured with separate soil cores collected immediately adjacent to the incubated cores. 4 replicate cores were collected each month and CO<sub>2</sub> flux measured by G2201-i CRDS analyser (Picarro, Santa Clara, California USA). The random forest models were built in Python v.3.9.13 and Jupyter Notebook v.6.4.12 using the RandomForestRegressor from the SciKit-Learn package v.1.0.2. Using the datasets uploaded, 80% of data was randomly selected and used for training, with the remaining 20% used for validation. To improve model performance, hyperparameter tuning was used to maximize the R<sup>2</sup> metric and 5-fold cross-validation used to assess how the model generalizes to unseen data and reduces the risk of overfitting. Subsequently, a baseline accuracy threshold was defined for feature selection, where R<sup>2</sup> ≥ 0.6 and the average 5-fold cross-validation (CV) score ≥ 0.4. Backward elimination of variables based on these performance metrics was systematically performed to maximise the number of variables included within each model without sacrificing model performance to ensure the maximum predictive power running ~120,000 model iterations. These models were used to map feature importance of the variables retained from the feature selection stage.These models were used to present the results in: J Breavington, A Steckbauer, C Fu, M Ennasri, and CM. Duarte. 'Dynamics of CO2 and CH4 fluxes in Red Sea mangrove soils'.

Cited by 1Open access

Random Forests Variable Importance.

George Crowley (4445893), James Kim (614437), Sophia Kwon (150240) et al. · Figshare · 2021

&lt;p&gt;&lt;b&gt;A.&lt;/b&gt; Mean decrease accuracy was used to determine and rank the top 5% of important metabolites, cytokines, chemokines, and clinical biomarkers. &lt;b&gt;B. Agglomerative, Hierarchical Clustering&lt;/b&gt; identified 5 clusters of variables in the refined profile with similar patterns of expression in the metabolomics subcohort. &lt;b&gt;C. PCA Loading Weights Plot&lt;/b&gt; visualizes clusters of variables based on intervariable correlations and provides an alternative-but-similar view of variable relationships in the metabolomics subcohort. Points are colored according to cluster membership.&lt;/p&gt;

Cited by 0Open access

On some random variables involving Bernoulli random variable

Romeo Meštrović · arXiv (Cornell University) · 2018

Motivated by the recent investigations given in [25] and the fact that Bernoulli probability-type models were often used in the study on some problems in theory of compressive sensing, here we define and study the complex-valued discrete random variables $\widetilde{X}_l(m,N)$ ($0\le l\le N-1$, $1\le m\le N$). Each of these random variables is defined as a linear combination of $N$ independent identically distributed $0-1$ Bernoulli random variables. We prove that for $l\not=0$, $\widetilde{X}_l(m,N)$ is the zero-mean random variable, and we also determine the variance of $\widetilde{X}_l(m,N)$ and its real and imaginary parts. Notice that $\widetilde{X}_l(m,N)$ belongs to the class of sub-Gaussian random variables that are significant in some areas of theory of compressive sensing. In particular, we prove some probability estimates for the mentioned random variables. These estimates are used to establish the upper bounds of the sub-Gaussian norm of their real and imaginary parts. We believe that our results should be implemented in certain applications of sub-Gaussian random variables for solving some problems in compressive sensing of sparse signals.

Cited by 1Open access