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Credibility via imprecise probability

Marco Zaffalon
2005 International Journal of Approximate Reasoning  
This kind of algorithmic imprecision is another important feature of imprecise probabilities, that allows credibility to be maintained despite computational limitations.  ...  The IDM might help in addressing this problem, since by exploiting the imprecision represented by lower and upper probabilities, it produces credible probabilities for any size of the learning data.  ... 
doi:10.1016/j.ijar.2004.11.001 fatcat:nyqfxqxw4bfvbgsogskm22ojue

A New Approach for the Evaluation of Structural Failure by Credibility Distribution

Palash Dutta, Nisha Gohain
2020 Open Civil Engineering Journal  
credibility distribution.  ...  Furthermore, it is encountered that when hybridization problems i.e., representation of imprecise components in the problem of structural failure, are both fuzzy and probabilistic nature, then the failure  ...  Using credibility sampling for credibility distributions Φ w , Φ Mo and Monte-Carlo sampling for probability distribution (CDF) F b ; three values Φ Here, if g 1 > 0 then credibility value (Cr 1 ) = 0  ... 
doi:10.2174/1874149502014010217 fatcat:ve33ck4kzvgsjkahxw2rf3offa

A comparison of statistical approaches for assessing reliability

Jason Matthew Aughenbaugh, Jeffrey W. Herrmann
2008 International Journal of Reliability and Safety  
We show that an imprecise beta model is compatible with both the robust Bayesian approach and the imprecise probability approach.  ...  probability approach.  ...  The imprecise beta model allows the designer to use ranges to express his beliefs about the value of the parameter (via a range of means) and the importance of the prior data (via a range of learning parameters  ... 
doi:10.1504/ijrs.2008.022077 fatcat:nusu4b4l6fdkbamrgu4dbwrjri

Inference for the shape parameter of lognormal distribution in presence of fuzzy data

Abbas Pak
2016 Pakistan Journal of Statistics and Operation Research  
Also, Bayes estimate and the corresponding highest posterior density credible interval of the unknown parameter are obtained by using Markov Chain Monte Carlo technique.  ...  Classical statistical procedures are not appropriate to deal with such imprecise cases. Fuzzy numbers are well used to model the imprecision of data.  ...  Also, the estimation via moments method has been presented by an iterative process. We have further constructed approximate confidence interval and HPD credible interval of the unknown parameter.  ... 
doi:10.18187/pjsor.v12i1.1084 fatcat:yec5cixocjhmplrdvg7qklwqs4

Statistical Inference of Kumaraswamy Distribution under Imprecise Information

Indranil Ghosh
2017 Journal of Biometrics & Biostatistics  
However, in real world situations, some information about an underlying experimental process might be imprecise and might be represented in the form of fuzzy information.  ...  The estimation procedures are discussed in details and compared via Markov Chain Monte Carlo simulations in terms of their average biases and mean squared errors.  ...  X  associated with it, each probability measure  θ on (Ω, ) induces a probability measure on X  defined as follows: Definition 3 The probability distribution on X  induced by θ is the mapping   ... 
doi:10.4172/2155-6180.1000378 fatcat:bpglgr5q6jgctc2hqzbyo3qf5u

Human Health Risk Assessment Under Uncertain Environment and Its SWOT Analysis

Palash Dutta
2018 The Open Public Health Journal  
Similarly, the emerging development of credibility theory can be considered as one of the uncertainty modelling tools that also has the ability to transform fuzzy variable into credibility distribution  ...  uncertainty/imprecision is involved such as clinical/medical decision making, economics, industrial cost-benefit analysis, other decision making process, etc.  ...  Depending on the features and accessibility of data, uncertainty could be modelled via type-I or interval valued fuzzy set and Credibility theory and possibility theory.  ... 
doi:10.2174/1874944501811010072 fatcat:ng3o3sqj55d53puprswiaezyxi

Statistical Inference of Kumaraswamy distribution under imprecise information [article]

Indranil Ghosh
2017 arXiv   pre-print
The estimation procedures are discussed in details and compared via Markov Chain Monte Carlo simulations in terms of their average biases and mean squared errors.  ...  However, in real world situations, some information about an underlying experimental process might be imprecise and might be represented in the form of fuzzy information.  ...  Table 2 : 2 Averages values and mean squared errors of the Bayes estimates of a and b, coverage probabilities and expected width of 95% credible interval for different sample sizes.  ... 
arXiv:1711.00149v1 fatcat:s6ev634j6bfstjsoziyccyw5b4

Imprecise Imputation: A Nonparametric Micro Approach Reflecting the Natural Uncertainty of Statistical Matching with Categorical Data

Eva Endres, Paul Fink, Thomas Augustin
2019 Journal of Official Statistics  
Additionally, we show how the results of imprecise imputation can be embedded into the theory of finite random sets, providing tight lower and upper bounds for probability statements.  ...  Altogether, we discuss three imprecise imputation strategies and propose ideas for potential refinements.  ...  Conditioning Disjunctive Random Sets The representation via the set M(P * ) of compatible probability distributions including the embedding into the framework of imprecise probabilities guides the further  ... 
doi:10.2478/jos-2019-0025 fatcat:kwiu5hpc2rflpcku2i6vehftyu

Robust Estimators under the Imprecise Dirichlet Model [article]

Marcus Hutter
2003 arXiv   pre-print
Yet, to be useful in practice, one needs efficient ways for computing the imprecise=robust sets or intervals.  ...  The main objective of this work is to derive exact, conservative, and approximate, robust and credible interval estimates under the IDM for a large class of statistical estimators, including the entropy  ...  Sets of probability distributions are often called Imprecise probabilities, hence the name IDM for this model. We avoid the term imprecise and use robust instead, or capitalize Imprecise.  ... 
arXiv:math/0305121v1 fatcat:ev5zxmjjsbeupnqhxfkzxzdhom

Practical robust estimators for the imprecise Dirichlet model

Marcus Hutter
2009 International Journal of Approximate Reasoning  
Yet, to be useful in practice, one needs efficient ways for computing the imprecise = robust sets or intervals.  ...  Walley's imprecise Dirichlet model (IDM) for categorical i.i.d. data extends the classical Dirichlet model to a set of priors.  ...  Sets of probability distributions are often called Imprecise probabilities, hence the name IDM for this model. We avoid the term imprecise and use robust instead, or capitalize Imprecise.  ... 
doi:10.1016/j.ijar.2008.03.020 fatcat:nixglzqumzd4ffpmpu4op7p76u

Credible classification for environmental problems

M ZAFFALON
2005 Environmental Modelling & Software  
Classifiers that aim at doing credible predictions should rely on carefully elicited prior knowledge.  ...  With credal classification, conditions of ignorance may limit the power of the inferences, not the credibility of the predictions.  ...  Credal classifiers deal with prior and likelihood ignorance by incorporating ignorance in the model, using imprecise probabilities, so that they can be more credible models for environmental problems.  ... 
doi:10.1016/j.envsoft.2004.10.006 fatcat:rc6uz7j645cbhjb6fzxlirjoze

Decision-Making Algorithm for Multisensor Fusion Based on Grey Relation and DS Evidence Theory

Fang Ye, Jie Chen, Yibing Li, Jian Kang
2016 Journal of Sensors  
The innovative decision-making algorithm firstly obtains the sensor's credibility through the introduction of grey relation theory and then defines two impact factors as sensor's credibility and evidence's  ...  The proposed algorithm comprehensively takes consideration of sensor's credibility and evidence's overall discriminability, which can solve the uncertainty problems caused by inconsistence of sensors themselves  ...  Relative to probability theory [5] , DS evidence theory can settle imprecise data and has a more extensive application area.  ... 
doi:10.1155/2016/3954573 fatcat:hcfdlzvy3zc75okn337a7rrivm

System safety assessment under epistemic uncertainty: Using imprecise probabilities in Bayesian network

Nima Khakzad
2019 Safety Science  
/updated belief masses to posterior imprecise probabilities.  ...  Evidence theory is an effective tool for manipulating imprecise probabilities.  ...  Imprecise probabilities characterize the uncertainty of an event A through a lower probability P A ( ) _ and an upper probability P Ā ( ), resulting in less specific yet more credible outcomes (Kozine  ... 
doi:10.1016/j.ssci.2019.03.008 fatcat:hbtzoe3jojgybo4ysiqbfsot5i

Credibility Assessment, Common Law Trials and Fuzzy Logic [chapter]

Gerald T. G. Seniuk
2012 Applied Issues in Investigative Interviewing, Eyewitness Memory, and Credibility Assessment  
The ratings in a criminal trial are almost all at the imprecise verbal level-you are either guilty or not guilty. Although criminal proof is not a matter of probabilities ( R. v.  ...  by chance or via the use of demeanour evidence, they do not claim to attempt to achieve levels of 90%, the threshold level many would assign to proof beyond reasonable doubt.  ... 
doi:10.1007/978-1-4614-5547-9_2 fatcat:supfzof3hfgcbkb2w7obphxmcq

Network meta-analysis: users' guide for pediatricians

Reem Al Khalifah, Ivan D. Florez, Gordon Guyatt, Lehana Thabane
2018 BMC Pediatrics  
Conclusions: In this article we discuss how clinicians can evaluate the credibility of NMA methods, and how they can make judgments regarding the quality (certainty) of the evidence.  ...  the estimate from the data to produce a posterior probability and its credible interval.  ...  Indirect comparisons can be made via deduction from the common comparator. 1.1.  ... 
doi:10.1186/s12887-018-1132-9 pmid:29843665 pmcid:PMC5975630 fatcat:uuhqvmj2hvcrdbxukbfmfsjjzu
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