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Found 15 papers, 2 papers with code
Re-Envisioning Numerical Information Field Theory (NIFTy.re): A Library for Gaussian Processes and Variational Inference
Imaging is the process of transforming noisy, incomplete data into a space that humans can interpret.
Sparse Kernel Gaussian Processes through Iterative Charted Refinement (ICR)
Gaussian Processes (GPs) are highly expressive, probabilistic models.
Probabilistic Autoencoder using Fisher Information
In this architecture, the latent space uncertainty is not generated using an additional information channel in the encoder, but derived from the decoder, by means of the Fisher information metric.
Geometric variational inference
Efficiently accessing the information contained in non-linear and high dimensional probability distributions remains a core challenge in modern statistics.
Bayesian decomposition of the Galactic multi-frequency sky using probabilistic autoencoders
The three most significant feature maps encode astrophysical components: (1) The dense interstellar medium (ISM), (2) the hot and dilute regions of the ISM and (3) the CMB.
Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena Data Analysis, Statistics and Probability
Comparison of classical and Bayesian imaging in radio interferometry
CLEAN, the commonly employed imaging algorithm in radio interferometry, suffers from a number of shortcomings: in its basic version it does not have the concept of diffuse flux, and the common practice of convolving the CLEAN components with the CLEAN beam erases the potential for super-resolution; it does not output uncertainty information; it produces images with unphysical negative flux regions; and its results are highly dependent on the so-called weighting scheme as well as on any human choice of CLEAN masks to guiding the imaging.
Instrumentation and Methods for Astrophysics Applications
Metric Gaussian Variational Inference
We propose Metric Gaussian Variational Inference (MGVI) as a method that goes beyond mean-field.
A Bayesian Model for Bivariate Causal Inference
We address the problem of two-variable causal inference without intervention.
Encoding prior knowledge in the structure of the likelihood
This transformation is a special form of the reparametrization trick, flattens the hierarchy and leads to a standard Gaussian prior on all resulting parameters.
Correlated signal inference by free energy exploration
The inference of correlated signal fields with unknown correlation structures is of high scientific and technological relevance, but poses significant conceptual and numerical challenges.
Optimal Belief Approximation
The loss function that is obtained in the derivation is equal to the Kullback-Leibler divergence when normalized.
Stochastic determination of matrix determinants
Matrix determinants play an important role in data analysis, in particular when Gaussian processes are involved.
Data Analysis, Statistics and Probability Instrumentation and Methods for Astrophysics Computation Methodology
Signal inference with unknown response: Calibration-uncertainty renormalized estimator
The calibration of a measurement device is crucial for every scientific experiment, where a signal has to be inferred from data.
Improving self-calibration
Contemporary self-calibration schemes try to find a self-consistent solution for signal and calibration by exploiting redundancies in the measurements.
NIFTY - Numerical Information Field Theory - a versatile Python library for signal inference
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.
Instrumentation and Methods for Astrophysics Information Theory Mathematical Software Mathematical Physics Information Theory Mathematical Physics Data Analysis, Statistics and Probability Computation
