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Bayesian model selection for the validation of computer codes
unpublished
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'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
2018
Nuclear Engineering and Design
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2018 pre-print arXiv:1801.01782v1 fatcat:3cic4crsbfc3ll6kxa5o7efgaq
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
2018
Nuclear Engineering and Design
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2018 pre-print arXiv:1801.09261v1 fatcat:eh4ue7runjccriiunxfhx7tfwq
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]
2021
arXiv
pre-print
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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
2019
Frontiers in Psychology
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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
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, 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]
2020
Figshare
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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
2015
Journal of Statistical Software
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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
2018
International Journal of Engineering & Technology
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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
2015
Journal of Chemical Information and Modeling
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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
2018
PLoS ONE
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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]
2017
bioRxiv
pre-print
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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]
2020
arXiv
pre-print
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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
2020
Studies in Health Technology and Informatics
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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
2017
Software quality journal
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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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