Related papers: Approximate Bayesian Computation Applied to the Di…
We discuss the possibility of identification of point-like gamma-ray sources (PGS) with small scale dark matter (DM) clumps in our Galaxy. Gamma-rays are supposed to originate from annihilation of DM particles in the clumps, where…
Approximate Bayesian computation (ABC) methods perform inference on model-specific parameters of mechanistically motivated parametric statistical models when evaluating likelihoods is difficult. Central to the success of ABC methods is…
Model selection in the presence of intractable likelihoods remains a central challenge in Bayesian inference. Approximate Bayesian computation (ABC) provides a flexible likelihood-free framework, but its use for model choice is known to be…
Very recently, it was pointed out that there exists a population of gamma-ray sources without associations at other wavelengths which exhibit spectral features consistent with mono-energetic lines at energies of approximately 111 and 129…
Astrophysical sources outside the Milky Way, such as active galactic nuclei and star-forming galaxies, leave their imprint on the gamma-ray sky as nearly isotropic emission referred to as the Extragalactic Gamma-Ray Background (EGB). While…
We look at dark energy from a biology inspired viewpoint by means of the Approximate Bayesian Computation (ABC) and late time cosmological observations. We find that dynamical dark energy comes out on top, or in the ABC language naturally…
Approximate Bayesian computation (ABC) is a set of techniques for Bayesian inference when the likelihood is intractable but sampling from the model is possible. This work presents a simple yet effective ABC algorithm based on the…
The detailed origin of the diffuse gamma-ray background is still unknown. However, the contribution of unresolved sources is expected to induce small-scale anisotropies in this emission, which may provide a way to identify and constrain the…
Also known as likelihood-free methods, approximate Bayesian computational (ABC) methods have appeared in the past ten years as the most satisfactory approach to untractable likelihood problems, first in genetics then in a broader spectrum…
Annihilation of cosmologically distributed dark matter is predicted to produce a potentially observable flux of high energy photons. Neglecting the contribution from local structure, this signal is predicted to be virtually uniform on the…
This paper introduces a simple, general framework for likelihood-free Bayesian reinforcement learning, through Approximate Bayesian Computation (ABC). The main advantage is that we only require a prior distribution on a class of simulators…
Our understanding of the $\gamma$-ray sky has improved dramatically in the past decade, however, the unresolved $\gamma$-ray background (UGRB) still has a potential wealth of information about the faintest $\gamma$-ray sources pervading the…
Approximate Bayesian computation (ABC) is a widely used inference method in Bayesian statistics to bypass the point-wise computation of the likelihood. In this paper we develop theoretical bounds for the distance between the statistics used…
The diffuse extragalactic background light consists of the sum of the starlight emitted by galaxies through the history of the Universe, and it could also have an important contribution from the first stars, which may have formed before…
Notwithstanding the advent of the Gamma-ray Large Area Telescope, theoretical models predict that a significant fraction of the cosmic gamma-ray background (CGB), at the level of 20% of the currently measured value, will remain unresolved.…
We examine the brightness of the Cosmic Radio Background (CRB) by comparing the contribution from individual source counts to absolute measurements. We use a compilation of radio counts to estimate the contribution of detected sources to…
The light we receive from distant astrophysical objects carries information about their origins and the physical mechanisms that power them. The study of these signals, however, is complicated by the fact that observations are often a…
Approximate Bayesian computation (ABC) has become an essential tool for the analysis of complex stochastic models when the likelihood function is numerically unavailable. However, the well-established statistical method of empirical…
Standard approaches to Bayesian parameter inference in large scale structure assume a Gaussian functional form (chi-squared form) for the likelihood. This assumption, in detail, cannot be correct. Likelihood free inferences such as…
Deep optical and near-infrared galaxy counts are utilized to estimate the extragalactic background light (EBL) coming from normal galactic light in the universe. Although the slope of number-magnitude relation of the faintest counts is flat…