mathematicalmichael
commented
5 years ago six segments.
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[x] theory in middle
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[x] distribution left
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[ ] parameter ID right
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[x] plots broken up by input vs output space, both methods on same plots
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[x] problem setup at top of examples columns
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[x] show equations for the two approaches in the dead middle.
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[x] middle column: "A Novel Approach" has DCI formula first, then stats, with labels.
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[x] middle column: "what problem are you trying to solve?" and "what is the difference(s) in the two approaches?"
Bayesian Inference updates prior information with a data-likelihood function.
Data-Consistent Inversion updates an initial distribution by inverting a measure constructed from data.
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[ ] middle column: model of exponential decay with unknown rate
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[x] middle column: bare minimum necessary notation
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[x] middle column: references
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[x] Title: Using Push-forward and Pull-back Measures for Parameter Identification and Distribution Estimation
yentyu
Tian:
[x] Compare this to the poster right now from CSE and decide what content we want to grab from it.
[x] Discuss what we mean by "DCI" because from what I understand, this is theory, which belongs before Parameter identification. I think in general, the whole MT framework is just DCI now instead of "Consistent Bayes."
[x] What are the distribution problems you are thinking about? Are the hierarchical Bayes models where you try to fit sd/mean values for parameters id?