Merge branch 'main' into dev

Author: stefanradev93

.github/workflows/multiversion-docs.yaml

@@ -55,7 +55,7 @@ jobs:
         run: |
           cd ./gh-pages-dev
           git rm --quiet -rf .
-          cp -R ../repo/docs/* ./
+          cp -r ../repo/docs/. ./
           git config --local user.email ""
           git config --local user.name "github-actions"
           git add -A

README.md

@@ -20,7 +20,7 @@ fueled by continuous progress in generative AI and Bayesian inference.
 <picture>
   <source media="(prefers-color-scheme: dark)" srcset="./img/bayesflow_landing_dark.jpg">
   <source media="(prefers-color-scheme: light)" srcset="./img/bayesflow_landing_light.jpg">
-  <img alt="dsd" src="./img/bayesflow_landing_dark.jpg">
+  <img alt="Overview graphic on using BayesFlow. It is split in three columns: 1. Choose your backend: BayesFlow is based on Keras, so you can choose PyTorch, TensorFlow or JAX. 2. Define your simulator: You specify your simulator in Python, and use it to generate simulated data. 3. Choose your algorithm: You define a generative neural network that you can use for estimation after training." src="./img/bayesflow_landing_dark.jpg">
 </picture>
 </div>
 

bayesflow/adapters/__init__.py

@@ -1,3 +1,8 @@
+"""
+A collection of :py:class:`~bayesflow.adapters.Adapter` transforms, which tell BayesFlow how to interpret your
+:py:class:`~bayesflow.simulators.Simulator` output and plug it into neural networks for training and inference.
+"""
+
 from . import transforms
 from .adapter import Adapter
 

bayesflow/approximators/__init__.py

@@ -1,3 +1,8 @@
+"""
+A collection of :py:class:`~bayesflow.approximators.Approximator`\ s, which embody the inference task and the
+neural network components used to perform it.
+"""
+
 from .approximator import Approximator
 from .continuous_approximator import ContinuousApproximator
 from .point_approximator import PointApproximator

bayesflow/datasets/__init__.py

@@ -1,3 +1,9 @@
+"""
+A collection of `keras.utils.PyDataset <https://keras.io/api/utils/python_utils/#pydataset-class>`__\ s, which
+wrap your data-generating process (i.e., your :py:class:`~bayesflow.simulators.Simulator`) and thus determine the
+effective training strategy (e.g., online or offline).
+"""
+
 from .offline_dataset import OfflineDataset
 from .online_dataset import OnlineDataset
 from .disk_dataset import DiskDataset

bayesflow/diagnostics/__init__.py

@@ -1,3 +1,7 @@
+"""
+A collection of plotting utilities and metrics for evaluating trained :py:class:`~bayesflow.workflows.Workflow`\ s.
+"""
+
 from .metrics import root_mean_squared_error, calibration_error, posterior_contraction
 
 from .plots import (

bayesflow/distributions/__init__.py

@@ -1,3 +1,9 @@
+"""
+A collection of :py:class:`~bayesflow.distributions.Distribution`\ s,
+which represent the latent space for :py:class:`~bayesflow.networks.InferenceNetwork`\ s
+or the summary space of :py:class:`~bayesflow.networks.SummaryNetwork`\ s.
+"""
+
 from .distribution import Distribution
 from .diagonal_normal import DiagonalNormal
 from .diagonal_student_t import DiagonalStudentT

bayesflow/experimental/__init__.py

@@ -1,3 +1,7 @@
+"""
+Unstable or largely untested networks, proceed with caution.
+"""
+
 from .cif import CIF
 from .continuous_time_consistency_model import ContinuousTimeConsistencyModel
 from .free_form_flow import FreeFormFlow

bayesflow/metrics/__init__.py

@@ -1,3 +1,8 @@
+"""
+A collection of `keras.Metric <https://keras.io/api/metrics/base_metric/#metric-class>`__\ s for evaluating the
+performance of models.
+"""
+
 from . import functional
 from .maximum_mean_discrepancy import MaximumMeanDiscrepancy
 from .root_mean_squard_error import RootMeanSquaredError

bayesflow/networks/__init__.py

@@ -1,3 +1,7 @@
+"""
+A rich collection of neural network architectures for use in :py:class:`~bayesflow.approximators.Approximator`\ s.
+"""
+
 from .consistency_models import ConsistencyModel
 from .coupling_flow import CouplingFlow
 from .deep_set import DeepSet