相关论文: Approximate Bayesian Computation Applied to the Di…

The X-ray background (XRB) is produced by a large number of faint sources distributed over a wide range of redshifts. The XRB carries information on the spatial distribution and evolution of these sources. The goals of the paper are: 1. to…

We present the first interpretation of the new isotropic diffuse gamma-ray background (IGRB), measured by the Fermi Large Area Telescope (LAT), based on a statistical analysis. We demonstrate that the gamma-ray emission from unresolved…

Approximate Bayesian computation (ABC) is commonly used for parameter estimation and model comparison for intractable simulator-based models whose likelihood function cannot be evaluated. In this paper we instead investigate the feasibility…

Annihilating dark matter (DM) has been discussed as a possible source of gamma-rays from the galactic center (GC) and contributing to the extragalactic gamma-ray background (EGB). Assuming universality of the density profile of DM halos, we…

Approximate Bayesian computation (ABC) is a likelihood-free inference method that has been employed in various applications. However, ABC can be sensitive to outliers if a data discrepancy measure is chosen inappropriately. In this paper,…

In recent years dynamical modelling has been provided with a range of breakthrough methods to perform exact Bayesian inference. However it is often computationally unfeasible to apply exact statistical methodologies in the context of large…

Photons produced in the annihilations of dark matter particles can be detected by gamma-ray telescopes; this technique of indirect detection serves as a cornerstone of the upcoming assault on the dark matter paradigm. The main obstacle to…

The existence of a cosmic background of primordial gravitational waves (PGWB) is a robust prediction of inflationary cosmology, but it has so far evaded discovery. The most promising avenue of its detection is via measurements of Cosmic…

Approximate Bayesian Computation (ABC) methods are commonly used to approximate posterior distributions in models with unknown or computationally intractable likelihoods. Classical ABC methods are based on nearest neighbor type algorithms…

The diffuse cosmic X-ray background (CXB) is the sum of the emission of discrete sources, mostly massive black-holes accreting matter in active galactic nuclei (AGN). The CXB spectrum differs from the integration of the spectra of…

Both Approximate Bayesian Computation (ABC) and composite likelihood methods are useful for Bayesian and frequentist inference, respectively, when the likelihood function is intractable. We propose to use composite likelihood score…

This paper provides a review of Approximate Bayesian Computation (ABC) methods for carrying out Bayesian posterior inference, through the lens of density estimation. We describe several recent algorithms and make connection with traditional…

Scientists often express their understanding of the world through a computationally demanding simulation program. Analyzing the posterior distribution of the parameters given observations (the inverse problem) can be extremely challenging.…

Approximate Bayesian computation (ABC) is a popular likelihood-free inference method for models with intractable likelihood functions. As ABC methods usually rely on comparing summary statistics of observed and simulated data, the choice of…

Complicated generative models often result in a situation where computing the likelihood of observed data is intractable, while simulating from the conditional density given a parameter value is relatively easy. Approximate Bayesian…

Approximate Bayesian computation (ABC) is a method for Bayesian inference when the likelihood is unavailable but simulating from the model is possible. However, many ABC algorithms require a large number of simulations, which can be costly.…

Many statistical models can be simulated forwards but have intractable likelihoods. Approximate Bayesian Computation (ABC) methods are used to infer properties of these models from data. Traditionally these methods approximate the posterior…

Approximate Bayesian computation (ABC) is an approach for sampling from an approximate posterior distribution in the presence of a computationally intractable likelihood function. A common implementation is based on simulating model,…

A maximum likelihood methodology for a general class of models is presented, using an approximate Bayesian computation (ABC) approach. The typical target of ABC methods are models with intractable likelihoods, and we combine an ABC-MCMC…

We use the recent measurement of the isotropic $\gamma$-ray background (IGRB) by Fermi LAT and analysis of the contribution of unresolved point $\gamma$-ray sources to IGRB to build constraints on the models of ultra-high cosmic rays…