scholarly journals BAYESIAN APPROACH TO ACCOUNTING FOR EPISTEMIC UNCERTAINTY OF PARAMETERS OF PROBABILISTIC MODELS OF RISK ANALYSIS OF DECISIONS

2020 ◽  
Vol 1 (№11 2020) ◽  
pp. 67-72
Author(s):  
A.I. Ptushkin ◽  
D.V. Reshetnikov ◽  
D.V. Shapovalov ◽  
A.N. Stepenko
Keyword(s):  
Risk Analysis ◽  
Bayesian Approach ◽  
Probabilistic Models ◽  
Epistemic Uncertainty ◽  
Uncertainty Of Parameters

ASSESSMENT OF RISK OF SAFETY IN NETWORKS OF THE INTERNET OF THINGS

2020 ◽  
pp. 561-566
Author(s):  
Андрей Александрович Болгов ◽  
Сергей Александрович Ермаков ◽  
Лариса Владимировна Паринова ◽  
Николай Ильич Баранников ◽  
Владимир Павлович Лось ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Risk Analysis ◽  
Probabilistic Models ◽  
The Internet ◽  
Response Characteristics ◽  
Common Problems ◽  
Traditional Approaches ◽  
The Internet Of Things

В статье предлагаются результаты анализа возможности применения традиционных подходов к анализу рисков в сетях Интернета вещей с учетом особенностей архитектуры построения и динамики их развития. До настоящего времени было предложено множество методов для решения таких проблем с использованием вероятностных моделей. Но несмотря на то, что они позволяют решить большинство задач, они все же могут вызывать некоторые проблемы при оценке рисков и анализе полученных результатов. Наиболее распространенные проблемы связаны со сложностью ранжирования и объективностью оценки вероятности нанесения ущерба и величины этого ущерба. По итогу к заключению статьи приводятся аргументы в пользу альтернативных методологий анализа рисков, адекватно учитывающих динамические характеристики технологии при сохранении преимуществ существующих подходов к оценке. In this article results of the analysis of possibility of application of traditional approaches to risk analysis in networks of the Internet of things taking into account features of architecture of creation and dynamics of their development are offered. So far, many methods have been proposed to solve such problems using probabilistic models. However, although they can solve most problems, they can still cause some problems when assessing risks and analyzing the results. The most common problems are related to the complexity of ranking and the objectivity of assessing the probability of damage and the magnitude of this damage. As a result, the article concludes with arguments in favor of the alternative methodologies of risk analysis which are adequately considering response characteristics of technology when saving advantages of the existing approaches to assessment are adduced.

Risk analysis in environmental systems

2002 ◽  
Vol 29 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Elhadi Shakshuki ◽  
Kumaraswamy Ponnambalam ◽  
Tassew Wodaj
Keyword(s):  
Risk Analysis ◽  
Linear Systems ◽  
Large Scale ◽  
Probabilistic Models ◽  
Large Scale Systems ◽  
Second Moment ◽  
Environmental Systems ◽  
Inherent Feature ◽  
The Moment ◽  
Uncertainty Information

Uncertainty is an inherent feature of environmental systems, which makes probabilistic models important. Environmental risk assessment is an important but time consuming task. For large-scale systems, use of linear systems with uncertainty information on parameters and inputs is one of the few possible methods to assess risk. To estimate risk, it is necessary to have at least the first two moments of output variables. This paper describes an efficient method developed for second-moment analysis of linear systems with uncertain coefficients. The main objective is to provide the means and the variances of the output and to provide efficient formulation and automation of the moment equations. This method is demonstrated in two real-world applications of environmental modeling.Key words: uncertainty, second-moment methods, risk analysis, reliability, linear systems.

Quantitative risk analysis of offshore drilling operations: A Bayesian approach

2013 ◽  
Vol 57 ◽  
pp. 108-117 ◽  
Author(s):  
Nima Khakzad ◽  
Faisal Khan ◽  
Paul Amyotte
Keyword(s):  
Risk Analysis ◽  
Bayesian Approach ◽  
Offshore Drilling ◽  
Quantitative Risk Analysis ◽  
Drilling Operations

The Bayesian approach in risk analysis

1986 ◽  
Vol 1 (2) ◽  
pp. 113-115
Author(s):  
R.E. Barlow
Keyword(s):  
Risk Analysis ◽  
Bayesian Approach ◽  
The Bayesian Approach

scholarly journals A Bayesian approach to integrate temporal data into probabilistic risk analysis of monitored NAPL remediation

2012 ◽  
Vol 36 ◽  
pp. 108-120 ◽  
Author(s):  
Daniel Fernàndez-Garcia ◽  
Diogo Bolster ◽  
Xavier Sanchez-Vila ◽  
Daniel M. Tartakovsky
Keyword(s):  
Risk Analysis ◽  
Bayesian Approach ◽  
Temporal Data ◽  
Probabilistic Risk Analysis ◽  
Probabilistic Risk

scholarly journals Risk analysis of animal–vehicle crashes: a hierarchical Bayesian approach to spatial modelling

2016 ◽  
Vol 21 (6) ◽  
pp. 614-626 ◽  
Author(s):  
Andrew Murphy ◽  
Jianhong (Cecilia) Xia
Keyword(s):  
Risk Analysis ◽  
Bayesian Approach ◽  
Spatial Modelling ◽  
Hierarchical Bayesian ◽  
Vehicle Crashes

Bayesian Approach to Quantifying Epistemic Uncertainty in a Processor Availability model

2012 ◽  
Vol 49 (6) ◽  
pp. 1019-1031 ◽  
Author(s):  
Chester J. Everline
Keyword(s):  
Bayesian Approach ◽  
Epistemic Uncertainty ◽  
Availability Model

scholarly journals EMPIRICAL COMPARISON OF METHODS FOR THE HIERARCHICAL PROPAGATION OF HYBRID UNCERTAINTY IN RISK ASSESSMENT, IN PRESENCE OF DEPENDENCES

2012 ◽  
Vol 20 (04) ◽  
pp. 509-557 ◽  
Author(s):  
NICOLA PEDRONI ◽  
ENRICO ZIO
Keyword(s):  
Risk Analysis ◽  
Epistemic Uncertainty ◽  
Probability Distributions ◽  
Uncertainty Propagation ◽  
Random Sets ◽  
Uncertain Parameters ◽  
Random Events ◽  
Possibility Distributions ◽  
Hybrid Uncertainty ◽  
Analysis Models

Risk analysis models describing aleatory (i.e., random) events contain parameters (e.g., probabilities, failure rates, …) that are epistemically-uncertain, i.e., known with poor precision. Whereas aleatory uncertainty is always described by probability distributions, epistemic uncertainty may be represented in different ways (e.g., probabilistic or possibilistic), depending on the information and data available. The work presented in this paper addresses the issue of accounting for (in)dependence relationships between epistemically-uncertain parameters. When a probabilistic representation of epistemic uncertainty is considered, uncertainty propagation is carried out by a two-dimensional (or double) Monte Carlo (MC) simulation approach; instead, when possibility distributions are used, two approaches are undertaken: the hybrid MC and Fuzzy Interval Analysis (FIA) method and the MC-based Dempster-Shafer (DS) approach employing Independent Random Sets (IRSs). The objectives are: i) studying the effects of (in)dependence between the epistemically-uncertain parameters of the aleatory probability distributions (when a probabilistic/possibilistic representation of epistemic uncertainty is adopted) and ii) studying the effect of the probabilistic/possibilistic representation of epistemic uncertainty (when the state of dependence between the epistemic parameters is defined). The Dependency Bound Convolution (DBC) approach is then undertaken within a hierarchical setting of hybrid (probabilistic and possibilistic) uncertainty propagation, in order to account for all kinds of (possibly unknown) dependences between the random variables. The analyses are carried out with reference to two toy examples, built in such a way to allow performing a fair quantitative comparison between the methods, and evaluating their rationale and appropriateness in relation to risk analysis.

Sign in / Sign up

Export Citation Format

Share Document