Biological Data Analysis: Error and Uncertainty
نویسنده
چکیده
Uncertainty plays an important role in analyzing and minimizing the errors in a given experimental data set in order to increase the reliability and quality of the obtained data. In this paper, we study biological data which is subject to technical limitations and errors, rendering the gathered data likely to be uncertain. By using fundamental numerical analysis approach, we have targeted to statistically identify the uncertainties. So far, it is extremely functional when dealing with a giant data sample containing large variations. Thus, identifying the margin above the threshold error of uncertainties, we offer a novel statistical modeling and its perspective applications in data analysis.
منابع مشابه
A Bootstrap Interval Robust Data Envelopment Analysis for Estimate Efficiency and Ranking Hospitals
Data envelopment analysis (DEA) is one of non-parametric methods for evaluating efficiency of each unit. Limited resources in healthcare economy is the main reason in measuring efficiency of hospitals. In this study, a bootstrap interval data envelopment analysis (BIRDEA) is proposed for measuring the efficiency of hospitals affiliated with the Hamedan University of Medical Sciences. The propos...
متن کاملتخمین عدم قطعیت مدل شبیه سازی سیلاب HEC-HMS با استفاده از الگوریتم مونت کارلو زنجیره مارکوف
There are some parameters in hydrologic models that cannot be measured directly. Estimation of hydrologic model parameters by various approaches and different optimization algorithms are generally error-prone, and therefore, uncertainty analysis is necessary. In this study we used DREAM-ZS, Differential Evolution Adaptive Metropolis, to investigate uncertainties of hydrologic model (HEC-HMS) pa...
متن کاملModeling Critical Flow through Choke for a Gas-condensate Reservoir Based on Drill Stem Test Data
Gas-condensate reservoirs contain hydrocarbon fluids with characteristics between oil and gas reservoirs and a high gas-liquid ratio. Due to the large gas-liquid ratio, wellhead choke calculations using the empirical equations such as Gilbert may contain considerable error. In this study, using drill stem test (DST) data of a gas-condensate reservoir, coefficients of Gilbert equation was modifi...
متن کاملBayes, E-Bayes and Robust Bayes Premium Estimation and Prediction under the Squared Log Error Loss Function
In risk analysis based on Bayesian framework, premium calculation requires specification of a prior distribution for the risk parameter in the heterogeneous portfolio. When the prior knowledge is vague, the E-Bayesian and robust Bayesian analysis can be used to handle the uncertainty in specifying the prior distribution by considering a class of priors instead of a single prior. In th...
متن کاملDiscrete time robust control of robot manipulators in the task space using adaptive fuzzy estimator
This paper presents a discrete-time robust control for electrically driven robot manipulators in the task space. A novel discrete-time model-free control law is proposed by employing an adaptive fuzzy estimator for the compensation of the uncertainty including model uncertainty, external disturbances and discretization error. Parameters of the fuzzy estimator are adapted to minimize the estimat...
متن کاملبررسی عدم قطعیت مدل موازنه جرمی برای تخمین نرخ فرایندهای هوازی در محل دفن پسماندهای شهری
Background and Objective: The aim of this study was to assess the sensitivity and uncertainty analysis of a mass balance model to estimate the rate of aerobic processes in a landfill. Materials and Methods: Monte Carlo simulation is a common method to evaluate uncertainty of the results of a model. Here, we used a Monte Carlo (MC) simulation. The data obtained from the experiments were used as...
متن کامل