Search Results for author: Uroš Seljak

Found 8 papers, 6 papers with code

Flow Annealed Kalman Inversion for Gradient-Free Inference in Bayesian Inverse Problems

no code implementations20 Sep 2023 Richard D. P. Grumitt, Minas Karamanis, Uroš Seljak

For many scientific inverse problems we are required to evaluate an expensive forward model.

Fluctuation without dissipation: Microcanonical Langevin Monte Carlo

1 code implementation31 Mar 2023 Jakob Robnik, Uroš Seljak

In contrast to the common belief, we show that the fluctuation-dissipation theorem is not required because only the configuration space distribution, and not the full phase space distribution, needs to be canonical.

Probabilistic Autoencoder

4 code implementations Under review 2020 Vanessa Böhm, Uroš Seljak

The PAE is fast and easy to train and achieves small reconstruction errors, high sample quality, and good performance in downstream tasks.

Bayesian Inference Denoising +5

Normalizing Constant Estimation with Gaussianized Bridge Sampling

1 code implementation pproximateinference AABI Symposium 2019 He Jia, Uroš Seljak

Normalizing constant (also called partition function, Bayesian evidence, or marginal likelihood) is one of the central goals of Bayesian inference, yet most of the existing methods are both expensive and inaccurate.

Bayesian Inference

Uncertainty Quantification with Generative Models

1 code implementation22 Oct 2019 Vanessa Böhm, François Lanusse, Uroš Seljak

We develop a generative model-based approach to Bayesian inverse problems, such as image reconstruction from noisy and incomplete images.

Image Reconstruction Uncertainty Quantification

Kepler data analysis: non-Gaussian noise and Fourier Gaussian process analysis of star variability

1 code implementation2 Oct 2019 Jakob Robnik, Uroš Seljak

We develop a statistical analysis model of Kepler star flux data in the presence of planet transits, non-Gaussian noise, and star variability.

Earth and Planetary Astrophysics

Perturbation theory, effective field theory, and oscillations in the power spectrum

1 code implementation7 Sep 2015 Zvonimir Vlah, Uroš Seljak, Man Yat Chu, Yu Feng

Shell crossings lead to non-perturbative effects, and the PT ignorance can be quantified in terms of their ratio, which is also the transfer function squared in the absence of stochasticity.

Cosmology and Nongalactic Astrophysics

Lagrangian perturbation theory at one loop order: successes, failures, and improvements

no code implementations7 Oct 2014 Zvonimir Vlah, Uroš Seljak, Tobias Baldauf

We find that the analytic calculations are in a good agreement with the LPT simulations, but when compared to full N-body simulations, we find that while one loop calculations improve upon the Zel'dovich approximation in the power spectrum, they still significantly lack power.

Cosmology and Nongalactic Astrophysics

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