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Part 1: Document Description
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Citation |
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Title: |
Replication Data for: The Method of Forced Probabilities: a Computation Trick for Bayesian Model Evidence |
Identification Number: |
doi:10.18419/darus-2815 |
Distributor: |
DaRUS |
Date of Distribution: |
2022-10-26 |
Version: |
1 |
Bibliographic Citation: |
Banerjee, Ishani; Walter, Peter, 2022, "Replication Data for: The Method of Forced Probabilities: a Computation Trick for Bayesian Model Evidence", https://doi.org/10.18419/darus-2815, DaRUS, V1 |
Citation |
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Title: |
Replication Data for: The Method of Forced Probabilities: a Computation Trick for Bayesian Model Evidence |
Subtitle: |
MFP implementation codes on SIP |
Identification Number: |
doi:10.18419/darus-2815 |
Authoring Entity: |
Banerjee, Ishani (Universität Stuttgart) |
Walter, Peter (Universität Stuttgart) |
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Other identifications and acknowledgements: |
Guthke, Anneli |
Other identifications and acknowledgements: |
Mumford, Kevin G. |
Other identifications and acknowledgements: |
Nowak, Wolfgang |
Software used in Production: |
MATLAB |
Grant Number: |
RTG GRK1829 |
Grant Number: |
EXC 2075 - 390740016 |
Distributor: |
DaRUS |
Access Authority: |
Banerjee, Ishani |
Access Authority: |
Nowak, Wolfgang |
Depositor: |
Banerjee, Ishani |
Date of Deposit: |
2022-04-14 |
Holdings Information: |
https://doi.org/10.18419/darus-2815 |
Study Scope |
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Keywords: |
Earth and Environmental Sciences, Engineering, Mathematical Sciences, Bayesian Model Selection, Markov-Chain Models, Markov Chain, Gas Injection in Porous Media |
Abstract: |
This dataset contains the codes used for implementing the method of forced probabilities of the manuscript: The Method of Forced Probabilities: A Computation Trick for Bayesian Model Evidence. Here, one can find the codes of implementation of the trick on stochastic invasion percolation (SIP) models discussed in the manuscript; it can be used by the reader to replicate the results of our paper.<br> Further, if the reader wants to apply the trick to his own model and data, this code can serve as a guideline on how to implement it. |
Kind of Data: |
Simulation Data |
Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Studies |
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The experimental data included in `scenarioReal/data/y0/y0_clean.mat` is binary data extracted and imported from the dataset of VanDeVen2021. The original data can be found here: https://doi.org/10.5683/SP2/RQKOCN |
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Related Publications |
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Citation |
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Title: |
Banerjee, I., Walter, P., Guthke, A., Mumford, K.G. & Nowak, W. (2022). The Method of Forced Probabilities: A Computation Trick for Bayesian Model Evidence. Computational Geosciences. Accepted for publication. |
Bibliographic Citation: |
Banerjee, I., Walter, P., Guthke, A., Mumford, K.G. & Nowak, W. (2022). The Method of Forced Probabilities: A Computation Trick for Bayesian Model Evidence. Computational Geosciences. Accepted for publication. |
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README.txt |
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add_evaluations_realData.m |
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BMEfactors.m |
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evaluate_BME_realData.m |
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load_exptruth.m |
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README.txt |
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runNew20Times.m |
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searching_subset.m |
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data_load_y0.m |
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importim.m |
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y0_clean.mat |
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application/matlab-mat |
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README_SIP.txt |
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SIP.m |
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eval_Trick.m |
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eval_Trick_multEvents.m |
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eval_Trick_noInit.m |
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GasClusSepn.m |
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generate_sub_exp.m |
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plotSIPFM.m |
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runGasFM.m |
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SIP_Ewing.m |
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ThreshCalc.m |
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Update_rule.m |
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Update_rule_trick.m |
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BMEfactors.m |
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BMEfactors_Scen1.m |
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gen_truth.m |
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load_truth.m |
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README.txt |
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evaluate_BME.m |
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add_evaluations_similarTe.m |
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evaluate_BME_similarTe.m |
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runNew20Times.m |
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add_evaluations_unknownTe.m |
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evaluate_BME_unknownTe.m |
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runNew20Times.m |
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README_SIP.txt |
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SIP.m |
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eval_Trick.m |
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eval_Trick_multEvents.m |
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eval_Trick_noInit.m |
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GasClusSepn.m |
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generate_sub_exp.m |
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plotSIPFM.m |
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runGasFM.m |
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SIP_Ewing_noReInv.m |
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ThreshCalc.m |
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Update_rule.m |
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Update_rule_trick.m |
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text/plain |