### 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 API reference API reference Development
 ... ... @@ -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 _. A visual comparison of the MGVI and GeoVI algorithm can be found in 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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