Abstract — In risk analysis, Bayesian methods are more adaptability and flexibility than traditional methods when be used to construct decision framework, estimate risk distribution and parameterize model, but has shortcomings at the same time. Robust methods make up some limitations of Bayesian methods, the analysis of uncertainty indicate that robust Bayesian methods can produce more reliable inference in the absence of comprehensive statistical information. Keywords-risk; uncertainty; Bayesian; robust I
Uncertainty refers to any limitation in knowledge. Identifying and characterizing uncertainty in con...
To study the effects of inaccuracies in the parameter probabilities of a Bayesian network, often a s...
Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide vari...
In recent years, we have seen a diverse range of crises and controversies concerning food safety, an...
Bayesian analysis constitutes an important pillar for assessing and managing risk, but it also has s...
Markov chain Monte Carlo (MCMC) approaches to sampling directly from the joint posterior distributio...
This book provides a multi-level introduction to Bayesian reasoning (as opposed to "conventional sta...
International audienceExplores methods for the representation and treatment of uncertainty in risk a...
We live in an era where every human entity, from a simple citizen to the head of an entity as large ...
International audienceThe notion of uncertainty has been a controversial issue for a long time. In p...
This chapter provides a general overview of Bayesian statistical methods. Topics include the notion ...
Assets are often classified according to their risk and expected return. The estimates of these para...
Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodo...
Probabilistic risk analysis aims to quantify the risk caused by high technology installations. Incre...
This paper compares Evidence Theory (ET) and Bayesian Theory (BT) for uncertainty modeling and decis...
Uncertainty refers to any limitation in knowledge. Identifying and characterizing uncertainty in con...
To study the effects of inaccuracies in the parameter probabilities of a Bayesian network, often a s...
Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide vari...
In recent years, we have seen a diverse range of crises and controversies concerning food safety, an...
Bayesian analysis constitutes an important pillar for assessing and managing risk, but it also has s...
Markov chain Monte Carlo (MCMC) approaches to sampling directly from the joint posterior distributio...
This book provides a multi-level introduction to Bayesian reasoning (as opposed to "conventional sta...
International audienceExplores methods for the representation and treatment of uncertainty in risk a...
We live in an era where every human entity, from a simple citizen to the head of an entity as large ...
International audienceThe notion of uncertainty has been a controversial issue for a long time. In p...
This chapter provides a general overview of Bayesian statistical methods. Topics include the notion ...
Assets are often classified according to their risk and expected return. The estimates of these para...
Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodo...
Probabilistic risk analysis aims to quantify the risk caused by high technology installations. Incre...
This paper compares Evidence Theory (ET) and Bayesian Theory (BT) for uncertainty modeling and decis...
Uncertainty refers to any limitation in knowledge. Identifying and characterizing uncertainty in con...
To study the effects of inaccuracies in the parameter probabilities of a Bayesian network, often a s...
Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide vari...