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Update landing img
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README.md

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@@ -16,14 +16,18 @@ fueled by continuous progress in generative AI and Bayesian inference.
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## Conceptual Overview
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<div align="center">
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<picture>
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<source media="(prefers-color-scheme: dark)" srcset="./img/bayesflow_landing_dark.jpg">
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<source media="(prefers-color-scheme: light)" srcset="./img/bayesflow_landing_light.jpg">
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<img alt="dsd" src="./img/bayesflow_landing_dark.jpg">
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</picture>
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</div>
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A cornerstone idea of amortized Bayesian inference is to employ generative
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neural networks for parameter estimation, model comparison, and model validation
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when working with intractable simulators whose behavior as a whole is too
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complex to be described analytically. The figure below presents a higher-level
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overview of neurally bootstrapped Bayesian inference.
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<img src="https://github.com/bayesflow-org/bayesflow/blob/master/img/high_level_framework.png?raw=true" width=80% height=80%>
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complex to be described analytically.
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## Disclaimer
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img/bayesflow_landing_dark.jpg

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img/bayesflow_landing_light.jpg

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img/high_level_framework.png

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img/showcase_calibration_curves.png

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img/showcase_confusion_matrix.png

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img/showcase_posterior.png

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img/showcase_recovery.png

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