Commit 9ed964a5 authored by Philipp Arras's avatar Philipp Arras
Browse files

nifty7 -> nifty8

parent 1ca6d03e
Pipeline #105250 passed with stages
in 28 minutes and 13 seconds
...@@ -34,7 +34,7 @@ html_theme_options = { ...@@ -34,7 +34,7 @@ html_theme_options = {
"icon_links": [ "icon_links": [
{ {
"name": "PyPI", "name": "PyPI",
"url": "https://pypi.org/project/nifty7", "url": "https://pypi.org/project/nifty8",
"icon": "fas fa-box", "icon": "fas fa-box",
} }
], ],
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...@@ -7,5 +7,5 @@ Welcome to the nifty8 documentation! ...@@ -7,5 +7,5 @@ Welcome to the nifty8 documentation!
:maxdepth: 1 :maxdepth: 1
User Guide <user/index> User Guide <user/index>
API reference <mod/nifty7> API reference <mod/nifty8>
Development <dev/index> Development <dev/index>
...@@ -11,7 +11,7 @@ As a compromise between being optimal and being computationally affordable, the ...@@ -11,7 +11,7 @@ As a compromise between being optimal and being computationally affordable, the
\int \mathcal{D}\xi \,\mathcal{Q}(\xi) \log \left( \frac{\mathcal{Q}(\xi)}{\mathcal{P}(\xi)} \right) \int \mathcal{D}\xi \,\mathcal{Q}(\xi) \log \left( \frac{\mathcal{Q}(\xi)}{\mathcal{P}(\xi)} \right)
NIFTy features two main alternatives for variational inference: Metric Gaussian Variational Inference (MGVI) and geometric Variational Inference (geoVI). NIFTy features two main alternatives for variational inference: Metric Gaussian Variational Inference (MGVI) and geometric Variational Inference (geoVI).
A visual comparison of the MGVI and GeoVI algorithm can be found in `variational_inference_visualized.py <https://gitlab.mpcdf.mpg.de/ift/nifty/-/blob/NIFTy_7/demos/variational_inference_visualized.py>`_. A visual comparison of the MGVI and GeoVI algorithm can be found in `variational_inference_visualized.py <https://gitlab.mpcdf.mpg.de/ift/nifty/-/blob/NIFTy_8/demos/variational_inference_visualized.py>`_.
Metric Gaussian Variational Inference (MGVI) Metric Gaussian Variational Inference (MGVI)
...@@ -45,7 +45,7 @@ Thus, only the gradient of the KL is needed with respect to this, which can be e ...@@ -45,7 +45,7 @@ Thus, only the gradient of the KL is needed with respect to this, which can be e
We stochastically estimate the KL-divergence and gradients with a set of samples drawn from the approximate posterior distribution. We stochastically estimate the KL-divergence and gradients with a set of samples drawn from the approximate posterior distribution.
The particular structure of the covariance allows us to draw independent samples solving a certain system of equations. The particular structure of the covariance allows us to draw independent samples solving a certain system of equations.
This KL-divergence for MGVI is implemented by This KL-divergence for MGVI is implemented by
:func:`~nifty7.minimization.kl_energies.MetricGaussianKL` within NIFTy7. :func:`~nifty8.minimization.kl_energies.MetricGaussianKL` within NIFTy8.
Note that MGVI typically provides only a lower bound on the variance. Note that MGVI typically provides only a lower bound on the variance.
...@@ -77,7 +77,7 @@ where :math:`\delta` denotes the Kronecker-delta. ...@@ -77,7 +77,7 @@ where :math:`\delta` denotes the Kronecker-delta.
GeoVI obtains the optimal expansion point :math:`\bar{\xi}` such that :math:`\mathcal{Q}_{\bar{\xi}}` matches the posterior as good as possible. GeoVI obtains the optimal expansion point :math:`\bar{\xi}` such that :math:`\mathcal{Q}_{\bar{\xi}}` matches the posterior as good as possible.
Analogous to the MGVI algorithm, :math:`\bar{\xi}` is obtained by minimization of the KL-divergence between :math:`\mathcal{P}` and :math:`\mathcal{Q}_{\bar{\xi}}` w.r.t. :math:`\bar{\xi}`. Analogous to the MGVI algorithm, :math:`\bar{\xi}` is obtained by minimization of the KL-divergence between :math:`\mathcal{P}` and :math:`\mathcal{Q}_{\bar{\xi}}` w.r.t. :math:`\bar{\xi}`.
Furthermore the KL is represented as a stochastic estimate using a set of samples drawn from :math:`\mathcal{Q}_{\bar{\xi}}` which is implemented in NIFTy7 via :func:`~nifty7.minimization.kl_energies.GeoMetricKL`. Furthermore the KL is represented as a stochastic estimate using a set of samples drawn from :math:`\mathcal{Q}_{\bar{\xi}}` which is implemented in NIFTy8 via :func:`~nifty8.minimization.kl_energies.GeoMetricKL`.
Publications Publications
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...@@ -3,7 +3,7 @@ NIFTy user guide ...@@ -3,7 +3,7 @@ NIFTy user guide
================ ================
This guide is an overview and explains the main idea behind nifty. More details This guide is an overview and explains the main idea behind nifty. More details
are found in the `API reference <../mod/nifty7.html>`_. are found in the `API reference <../mod/nifty8.html>`_.
.. toctree:: .. toctree::
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__version__ = '7.0' __version__ = '8.0'
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