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 = {
"icon_links": [
{
"name": "PyPI",
"url": "https://pypi.org/project/nifty7",
"url": "https://pypi.org/project/nifty8",
"icon": "fas fa-box",
}
],
......
......@@ -7,5 +7,5 @@ Welcome to the nifty8 documentation!
:maxdepth: 1
User Guide <user/index>
API reference <mod/nifty7>
API reference <mod/nifty8>
Development <dev/index>
......@@ -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)
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)
......@@ -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.
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
: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.
......@@ -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.
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
......
......@@ -3,7 +3,7 @@ NIFTy user guide
================
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::
......
__version__ = '7.0'
__version__ = '8.0'
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