Search Results for author:
Found 10 papers, 8 papers with code
Optimal simulation-based Bayesian decisions
We present a framework for the efficient computation of optimal Bayesian decisions under intractable likelihoods, by learning a surrogate model for the expected utility (or its distribution) as a function of the action and data spaces.
Fishnets: Information-Optimal, Scalable Aggregation for Sets and Graphs
Set-based learning is an essential component of modern deep learning and network science.
Marginal Post Processing of Bayesian Inference Products with Normalizing Flows and Kernel Density Estimators
Bayesian analysis has become an indispensable tool across many different cosmological fields including the study of gravitational waves, the Cosmic Microwave Background and the 21-cm signal from the Cosmic Dawn among other phenomena.
Nested sampling with any prior you like
In this letter we show that parametric bijectors trained on samples from a desired prior density provide a general-purpose method for constructing transformations from the uniform base density to a target prior, enabling the practical use of nested sampling under arbitrary priors.
Likelihood-free inference with neural compression of DES SV weak lensing map statistics
We present likelihood-free cosmological parameter inference using weak lensing maps from the Dark Energy Survey (DES) SV data, using neural data compression of weak lensing map summary statistics.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
SPECULATOR: Emulating stellar population synthesis for fast and accurate galaxy spectra and photometry
We present \textsc{speculator} -- a fast, accurate, and flexible framework for emulating stellar population synthesis (SPS) models for predicting galaxy spectra and photometry.
Instrumentation and Methods for Astrophysics Astrophysics of Galaxies
Nuisance hardened data compression for fast likelihood-free inference
This means that the nuisance marginalized inference task involves learning $n$ interesting parameters from $n$ "nuisance hardened" data summaries, regardless of the presence or number of additional nuisance parameters to be marginalized over.
Cosmology and Nongalactic Astrophysics
Fast likelihood-free cosmology with neural density estimators and active learning
Likelihood-free inference provides a framework for performing rigorous Bayesian inference using only forward simulations, properly accounting for all physical and observational effects that can be successfully included in the simulations.
Cosmology and Nongalactic Astrophysics
Prospects for resolving the Hubble constant tension with standard sirens
The Hubble constant ($H_0$) estimated from the local Cepheid-supernova (SN) distance ladder is in 3-$\sigma$ tension with the value extrapolated from cosmic microwave background (CMB) data assuming the standard cosmological model.
Cosmology and Nongalactic Astrophysics
Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology
Secondly, we present the first cosmological application of Density Estimation Likelihood-Free Inference (\textsc{delfi}), which learns a parameterized model for joint distribution of data and parameters, yielding both the parameter posterior and the model evidence.
Cosmology and Nongalactic Astrophysics
