Commit c61b7f37 authored by Philipp Frank's avatar Philipp Frank
Browse files

minor changes

parent 918324e1
Pipeline #102612 passed with stages
in 13 minutes and 41 seconds
......@@ -78,8 +78,8 @@ A new posterior approximation scheme, called geometric Variational Inference
that it uses (non-linear) geoVI samples instead of (linear) MGVI samples.
`GeoMetricKL` can be configured such that it reduces to `MetricGaussianKL`.
`GeoMetricKL` is now used in `demos/getting_started_3.py` and a visual
comparison to MGVI can be found in `demos/vi_visualized.py`. For further details
see (<https://arxiv.org/abs/2105.10470>).
comparison to MGVI can be found in `demos/variational_inference_visualized.py`.
For further details see (<https://arxiv.org/abs/2105.10470>).
LikelihoodOperator
......
......@@ -132,7 +132,7 @@ NIFTy takes advantage of this formulation in several ways:
3) The response can be non-linear, e.g. :math:`{R'(s)=R \exp(A\,\xi)}`, see `demos/getting_started_2.py`.
4) The amplitude operator may depend on further parameters, e.g. :math:`A=A(\tau)=e^{2\tau}` represents an amplitude operator with a positive definite, unknown spectrum.
4) The amplitude operator may depend on further parameters, e.g. :math:`A=A(\tau)=F\, \widehat{e^\tau}` represents an amplitude operator with a positive definite, unknown spectrum.
The log-amplitude field :math:`{\tau}` is modelled with the help of an integrated Wiener process in order to impose some (user-defined degree of) spectral smoothness.
5) NIFTy calculates the gradient of the information Hamiltonian and the Fisher information metric with respect to all unknown parameters, here :math:`{\xi}` and :math:`{\tau}`, by automatic differentiation.
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment