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Bayesian model selection for the validation of computer codes

Guillaume Damblin, Merlin Keller, Pierre Barbillon, Alberto Pasanisi, Eric Parent
unpublished
'Code Validation provides assurance that the models in the codes produce mathematically correct answers and that the answers reflect physical reality' Introduction Bayesian Code Validation Numerical Experiment  ...  Envisioned Industrial applications : Bayesian calibration of computer models. Journal of the Royal Statistical Society, Series B, Methodological, 63 :425-464.  ...  Power plant production control model (DYMOLA) Hydraulic model of Garrone river (TELEMAC-2D) → costly and high-dimensional (spatial) output  ... 
fatcat:wjcrulo3zrhetdif2srusishgm

Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, Part 1: Theory

Xu Wu, Tomasz Kozlowski, Hadi Meidani, Koroush Shirvan
2018 Nuclear Engineering and Design  
We provided a detailed introduction and comparison of the full and modular Bayesian approaches for inverse UQ, as well as pointed out their limitations when extrapolated to the validation/prediction domain  ...  The model discrepancy term is accounted for in our formulation through the "model updating equation".  ...  "Design of computer experiments" (Appendix A) is the process to select input locations to run the computer code and provide training samples for the GP emulator.  ... 
doi:10.1016/j.nucengdes.2018.06.004 fatcat:k2xivgdpxzcupjzvr25rzzlmpm

Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian Process, Part 2: Application to TRACE

Xu Wu, Tomasz Kozlowski, Hadi Meidani, Koroush Shirvan
2018 Nuclear Engineering and Design  
This sequential TSA methodology first selects experimental tests for validation that has a full coverage of the test domain to avoid extrapolation of model discrepancy term when evaluated at input setting  ...  The resulting posterior probability distributions of TRACE parameters can be used in future uncertainty, sensitivity and validation studies of TRACE code for nuclear reactor system design and safety analysis  ...  Method to select initial set for validation In our improved modular Bayesian approach outlined in Figure 1 , the computer code output M ( , ) is first obtained at the input settings of all the tests test  ... 
doi:10.1016/j.nucengdes.2018.06.003 fatcat:n6f2xcfyxvcghgjwv6blyfeequ

A Comprehensive Survey of Inverse Uncertainty Quantification of Physical Model Parameters in Nuclear System Thermal-Hydraulics Codes [article]

Xu Wu, Ziyu Xie, Farah Alsafadi, Tomasz Kozlowski
2021 arXiv   pre-print
Uncertainty Quantification (UQ) is an essential step in computational model validation because assessment of the model accuracy requires a concrete, quantifiable measure of uncertainty in the model predictions  ...  This review paper aims to provide a comprehensive and comparative discussion of the major aspects of the IUQ methodologies that have been used on the physical models in system thermal-hydraulics codes.  ...  Direct comparison of code simulations with experimental data for the selected phenomena can be used for IUQ.  ... 
arXiv:2104.12919v1 fatcat:tswg2ntnxrf4djaxsu63f6lcbu

Tracking the Time Course of Bayesian Inference With Event-Related Potentials:A Study Using the Central Cue Posner Paradigm

Carlos M. Gómez, Antonio Arjona, Francesco Donnarumma, Domenico Maisto, Elena I. Rodríguez-Martínez, Giovanni Pezzulo
2019 Frontiers in Psychology  
Estimates of prior expectation and surprise were obtained on a trial-by-trial basis from participants' responses, using a computational model implementing Bayesian learning.  ...  Three different types of blocks with validities of 50%, 64%, and 88%, respectively, were presented.  ...  This research has received funding from the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2 to GP).  ... 
doi:10.3389/fpsyg.2019.01424 pmid:31275215 pmcid:PMC6593096 fatcat:7fdrgtn6trfkbj675x7ovupsiq

Contents [chapter]

2020 Statistics, Data Mining, and Machine Learning in Astronomy  
, Malmquist, and Lutz-Kelker Biases 180 5.6 Simple Examples of Bayesian Analysis: Parameter Estimation 185 5.7 Simple Examples of Bayesian Analysis: Model Selection 211 5.8 Numerical Methods for  ...  Complex Problems (MCMC) 217 5.9 Hierarchical Bayesian Modeling 228 5.10 Approximate Bayesian Computation 232 5.11 Summary of Pros and Cons for Classical and Bayesian Methods 234 References 237  ... 
doi:10.1515/9780691197050-toc fatcat:mfzhkxb7qbfnjjxa4ypzxqu4qm

Bayesian model selection for statistical analysis of neural data: Lessons from fMRI [article]

Joram Soch
2020 Figshare  
github.com/JoramSoch/PycvBMScvLME package: https://github.com/JoramSoch/cvLMENeuroPixel dataset: http://data.cortexlab.net/dualPhase3/Talk announcement (1): https://www.bccn-berlin.de/talks/joram-soch-cross-validated-bayesian-model-selection.htmlTalk  ...  Technical University of BerlinSeminar: "Current Topics in Computational Neuroscience"Topic: "Statistical Analysis of Neural Data" (WS 2018/2019)Session: Wed, 13/02/2019, 10:15-11:45 a.m.cvBMS paper: https  ...  likelihood function prior distribution posterior distribution (model) "evidence" model evidence log model evidence The cross-validated LME 3.  ... 
doi:10.6084/m9.figshare.11973393.v1 fatcat:rzocjbafajgaxduvfz5pjnblmi

Statistical Modeling and Computation

Abdolvahab Khademi
2015 Journal of Statistical Software  
and psychometrics, among others; and (2) the exploitation of computing power both in enhancing statistical analysis and modeling and also design of new algorithms in implementing statistical methods.  ...  and the models.  ...  The Bayes factor is described in detail for Bayesian model selection with illustrative examples and MATLAB code.  ... 
doi:10.18637/jss.v066.b03 fatcat:4ld2eq2sarhehe7xj2f3dy6ksa

Technology analysis of artificial intelligence using Bayesian inference for neural networks

Sunghae Jun
2018 International Journal of Engineering & Technology  
We correct the patent documents related to AI technology, and analyze them using statistical modelling. We use Bayesian inference for neural networks to build our proposed method.  ...  To verify the validity of our research, we carry out a case study using the AI patent documents.  ...  Fig. 1 : 1 Technology analysis process by Bayesian inference for neural network models. Fig. 2 : 2 Boxplots of top 20 IPC codes used for input variables.  ... 
doi:10.14419/ijet.v7i2.3.9965 fatcat:d3kphnz6ivcntpcopt7e3czujq

Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets

Alex M. Clark, Krishna Dole, Anna Coulon-Spektor, Andrew McNutt, George Grass, Joel S. Freundlich, Robert C. Reynolds, Sean Ekins
2015 Journal of Chemical Information and Modeling  
Public accessibility is also an issue with computational models for bioactivity, and the ability to share such models still remains a major challenge limiting drug discovery.  ...  We have now described how the implementation of Bayesian models with FCFP6 descriptors generated in the CDD Vault enables the rapid production of robust machine learning models from public data or the  ...  , methods for extracting suitable validation test sets from large public datasets, automated determination of thresholds for active/inactive, and the impact of training set selection on internal cross-validation  ... 
doi:10.1021/acs.jcim.5b00143 pmid:25994950 pmcid:PMC4478615 fatcat:tsmnmd6m5bdwjba7sodppx3kk4

Bayesian validation of grammar productions for the language of thought

Sergio Romano, Alejo Salles, Marie Amalric, Stanislas Dehaene, Mariano Sigman, Santiago Figueira, Zaid Abdo
2018 PLoS ONE  
In this work we propose an extra validation step for the set of atomic productions defined by the experimenter.  ...  We then test this method in the language of geometry, a specific LoT model for geometrical sequence learning. Finally, despite the fact of the geometrical LoT not being a universal (i.e.  ...  Bayesian inference for LoT's productions The project of Bayesian analysis of the LoT models concept learning using Bayesian inference in a grammatically structured hypothesis space [25] .  ... 
doi:10.1371/journal.pone.0200420 pmid:29990351 pmcid:PMC6039029 fatcat:for3luinlvfkji27ehgq4ee6pe

Bayesian Selection Of Grammar Productions For The Language Of Thought [article]

Sergio Romano, Alejo Salles, Marie Amalric, Stanislas Dehaene, Mariano Sigman, Santiago Figueria
2017 bioRxiv   pre-print
We then test this method in the language of geometry, a specific LoT model (Amalric et al., 2017). Finally, despite the fact of the geometrical LoT not being a universal (i.e.  ...  Turing-complete) language, we show an empirical relation between a sequence's probability and its complexity consistent with the theoretical relationship for universal languages described by Levin's Coding  ...  This would not only provide empirical evidence about the adequacy of the choice of the original productions for the selected LoT but, more importantly, about the usefulness of Bayesian inference for selecting  ... 
doi:10.1101/141358 fatcat:ty4rfls7ancgpb4jgl22dcibba

Efficient compressive and Bayesian characterization of biphoton frequency spectra [article]

Emma M. Simmerman, Hsuan-Hao Lu, Andrew M. Weiner, Joseph M. Lukens
2020 arXiv   pre-print
Applying a custom Bayesian model to the same data, we then additionally realize reliable and consistent quantification of uncertainty.  ...  Here we introduce and compare compressive sensing and Bayesian mean estimation for recovering the spectral correlations of entangled photon pairs.  ...  ., for loaning the PPLN ridge waveguide and P. Lougovski for discussions. This research was performed in part at Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U.S.  ... 
arXiv:2003.04391v1 fatcat:mtbqckhufjc7boaaoam3fotr4u

Supervised Bayesian Statistical Learning to Identify Prognostic Risk Factor Patterns from Population Data

Colin J Crooks
2020 Studies in Health Technology and Informatics  
The model explained 92% of the observed variation in 5 year survival in the population.  ...  Current methods for building risk models assume averaged uniform effects across populations.  ...  Topic modelling also has computational advantages through Bayesian learning, as it can process larger numbers of codes, whilst it incorporates priors to reduce over fitting where the data is sparse.  ... 
doi:10.3233/shti200195 pmid:32570419 fatcat:z5hokogcerct7c5zpnn3thwc7m

Estimating software robustness in relation to input validation vulnerabilities using Bayesian networks

Ekincan Ufuktepe, Tugkan Tuglular
2017 Software quality journal  
We propose a method for estimating the robustness of software in relation to input validation vulnerabilities using Bayesian networks.  ...  It calculates a robustness value using information on the existence of input validation code in the functions and utilizing common weakness scores of known input validation vulnerabilities.  ...  In the second step, for each, existing validation code in the source code increases the probability of the BContaining Validation Code^of the input validation vulnerability.  ... 
doi:10.1007/s11219-017-9359-5 fatcat:spuuzv6zunainjyp73wij3cree
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