[no ci] README: move details on migration to FAQ

Author: vpratz

README.md

@@ -18,28 +18,14 @@ fueled by continuous progress in generative AI and Bayesian inference.
 
 You are currently looking at BayesFlow 2.0+, which is a complete rewrite of the library.
 While it shares the same overall goals with the 1.x versions, the API is not compatible.
-You can find the most recent version of BayesFlow 1.x on the `stable-legacy` branch.
-The latest [BayesFlow 1.x documentation](https://bayesflow.org/stable-legacy/index.html) can be accessed by selecting the "stable-legacy" entry in the version picker of the documentation.
 
 > [!CAUTION]
-> You should not upgrade (yet) if one of the following applies:
->
-> - You have an ongoing project that uses BayesFlow 1.x, and you do not want to allocate time for migrating to the new API.
-> - You require a feature that was not ported to BayesFlow 2.0+ yet. To our knowledge, this applies to:
->   * Two-level/Hierarchical models: `TwoLevelGenerativeModel`, `TwoLevelPrior`.
->   * Sensitivity analysis: functionality from the `bayesflow.sensitivity` module.
->   * MCMC (discontinued): The `bayesflow.mcmc` module. We are considering other options to enable the use of BayesFlow in an MCMC setting.
->   * Networks: `EvidentialNetwork`.
->   * Model misspecification detection: MMD test in the summary space (see #384).
-> - You have already trained models in BayesFlow 1.x, that you do not want to re-train with the new version. Loading models from version 1.x in version 2.0+ is not supported.
->
-> If you encounter any functionality that is missing and not listed here, please let us know by opening an issue.
-
-The new version brings many features, like multi-backend support via Keras3, and improved modularity and extensibility.
-We recommend to upgrade if none of the above conditions apply.
-Continue reading below for installation instructions and examples to get started.
-The [Moving from BayesFlow v1.1 to v2.0](examples/From_BayesFlow_1.1_to_2.0.ipynb) guide highlights how concepts and classes relate between the two versions.
-For additional information, please refer to the [FAQ](#faq) below.
+> A few features, most notably hierarchical models, have not been ported to BayesFlow 2.0+
+> yet. We are working on those features and plan to add them soon. You can find the complete
+> list in the [FAQ](#faq) below.
+
+The [Moving from BayesFlow v1.1 to v2.0](examples/From_BayesFlow_1.1_to_2.0.ipynb) guide
+highlights how concepts and classes relate between the two versions.
 
 ## Conceptual Overview
 
@@ -242,11 +228,47 @@ while the old version was based on TensorFlow.
 
 -------------
 
+**Question:**
+Should I switch to BayesFlow 2.0+ now? Are there features that are still missing?
+
+**Answer:**
+In general, we recommend to switch, as the new version is easier to use and will continue
+to receive improvements and new features. However, a few features are still missing, so you
+might want to wait until everything you need has been ported to BayesFlow 2.0+.
+
+Depending on your needs, you might not want to upgrade yet if one of the following applies:
+
+- You have an ongoing project that uses BayesFlow 1.x, and you do not want to allocate
+  time for migrating it to the new API.
+- You have already trained models in BayesFlow 1.x, that you do not want to re-train
+  with the new version. Loading models from version 1.x in version 2.0+ is not supported.
+- You require a feature that was not ported to BayesFlow 2.0+ yet. To our knowledge,
+  this applies to:
+  * Two-level/Hierarchical models: `TwoLevelGenerativeModel`, `TwoLevelPrior`.
+  * Sensitivity analysis: functionality from the `bayesflow.sensitivity` module.
+  * MCMC (discontinued): The `bayesflow.mcmc` module. We are considering other options
+    to enable the use of BayesFlow in an MCMC setting.
+  * Networks: `EvidentialNetwork`.
+  * Model misspecification detection: MMD test in the summary space (see #384).
+
+If you encounter any functionality that is missing and not listed here, please let us
+know by opening an issue.
+
+-------------
+
 **Question:**
 I still need the old BayesFlow for some of my projects. How can I install it?
 
 **Answer:**
 You can find and install the old Bayesflow version via the `stable-legacy` branch on GitHub.
+The corresponding [documentation](https://bayesflow.org/stable-legacy/index.html) can be
+accessed by selecting the "stable-legacy" entry in the version picker of the documentation.
+
+You can also install the latest version of BayesFlow v1.x from PyPI using
+
+```
+$ pip install "bayesflow<2.0"
+```
 
 -------------