# NIFTY - Numerical Information Field Theory - a versatile Python library for signal inference

@article{Selig2013NIFTYN, title={NIFTY - Numerical Information Field Theory - a versatile Python library for signal inference}, author={Marco Selig and Michael R. Bell and H. Junklewitz and Niels Oppermann and Martin Reinecke and Maksim Greiner and Carlos Pachajoa and Torsten A. Ensslin}, journal={ArXiv}, year={2013}, volume={abs/1301.4499} }

NIFTy, “Numerical Information Field Theory”, is a software package designed to enable the development of signal inference algorithms that operate regardless of the underlying spatial grid and its resolution. Its object-oriented framework is written in Python, although it accesses libraries written in Cython, C++, and C for eciency. NIFTy oers a toolkit that abstracts discretized representations of continuous spaces, fields in these spaces, and operators acting on fields into classes. Thereby…

## 51 Citations

### NIFTy 3 – Numerical Information Field Theory: A Python Framework for Multicomponent Signal Inference on HPC Clusters

- Computer ScienceAnnalen der Physik
- 2019

NIFTy 3 allows the user to run inference algorithms on massively parallel high performance computing clusters without changing the implementation of the field equations, and supports n‐dimensional Cartesian spaces, spherical spaces, power spaces, and product spaces as well as transforms to their harmonic counterparts.

### Information Theory for Fields

- Computer Science, PhysicsAnnalen der Physik
- 2019

Information field theory is built upon the language of mathematical physics, in particular, on field theory and statistical mechanics and permits the mathematical derivation of optimal imaging algorithms, data analysis methods, and even computer simulation schemes.

### Matrix-free large-scale Bayesian inference in cosmology

- Computer Science
- 2015

A new matrix-free implementation of the Wiener sampler which is traditionally applied to high dimensional analysis when signal covariances are unknown is proposed, permitting to cast the complex multivariate inference problem into a sequence of uni-variate random processes.

### Information Field Theory and Artificial Intelligence

- Computer ScienceEntropy
- 2022

This paper reformulated the process of inference in IFT in terms of GNN training, suggesting that IFT is well suited to address many problems in AI and ML research and application.

### Denoising, deconvolving, and decomposing photon observations - Derivation of the D3PO algorithm

- Computer Science
- 2015

The D 3 PO algorithm addresses the inference problem of denoising, deconvolving, and decomposing photon observations and successfully denoised, deconvolved, and decomposed the data into a diuse and a point-like signal estimate for the respective photon flux components.

### Encoding prior knowledge in the structure of the likelihood

- Computer ScienceArXiv
- 2018

This work investigates a specific transformation of the model parameters based on the multivariate distributional transform, a special form of the reparametrization trick, which flattens the hierarchy and leads to a standard Gaussian prior on all resulting parameters.

### Metric Gaussian Variational Inference

- Computer ScienceArXiv
- 2019

The proposed Metric Gaussian Variational Inference (MGVI) is an iterative method that performs a series of Gaussian approximations to the posterior that achieves linear scaling by avoiding to store the covariance explicitly at any time.

### Denoising, deconvolving, and decomposing multi-domain photon observations

- PhysicsAstronomy & Astrophysics
- 2018

D4PO successfully denoised, deconvolved, and decomposed a photon count image into diffuse, point-like and background flux, each being functions of location as well as energy.

### Reconstruction of Gaussian and log-normal fields with spectral smoothness

- Computer Science
- 2013

It is shown that the minimization of the Gibbs free energy, corresponding to a Gaussian approximation to the posterior marginalized over the power spectrum, is equivalent to the empirical Bayes ansatz, in which the power Spectrum is fixed to its maximum a posteriori value.

### Fast and precise way to calculate the posterior for the local non-Gaussianity parameter fnl from cosmic microwave background observations

- Physics
- 2013

We present an approximate calculation of the full Bayesian posterior probability distribution for the local non-Gaussianity parameter $f_{\text{nl}}$ from observations of cosmic microwave background…

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