相关论文: Approximate Bayesian Computation Applied to the Di…
A probabilistic technique for the joint estimation of background and sources with the aim of detecting faint and extended celestial objects is described. Bayesian probability theory is applied to gain insight into the coexistence of…
We present a new statistical method for constructing background subtracted measurements from event list data gathered by X-ray and gamma ray observatories. This method was initially developed specifically to construct images that account…
Approximate Bayesian Computation (ABC) methods are applicable to statistical models specified by generative processes with analytically intractable likelihoods. These methods try to approximate the posterior density of a model parameter by…
Many scientifically well-motivated statistical models in natural, engineering, and environmental sciences are specified through a generative process. However, in some cases, it may not be possible to write down the likelihood for these…
Detecting the dark matter annihilation signal from Galactic substructure, or subhalos, is an important challenge for high-energy gamma-ray experiments. In this paper we discuss detection prospects by combining two different aspects of the…
The nature of the Isotropic $\gamma$-ray Background (IGRB) measured by the Large Area Telescope (LAT) on the Fermi $\gamma$-ray space Telescope ({\it Fermi}) remains partially unexplained. Non-negligible contributions may originate from…
This invited feature article introduces and provides an extensive simulation study of a new Approximate Bayesian Computation (ABC) framework for estimating the posterior distribution and the maximum likelihood estimate (MLE) of the…
Several cosmic ray experiments have measured excesses in electrons and positrons, relative to standard backgrounds, for energies from ~ 10 GeV - 1 TeV. These excesses could be due to new astrophysical sources, but an explanation in which…
We discuss an approach for deriving robust posterior distributions from $M$-estimating functions using Approximate Bayesian Computation (ABC) methods. In particular, we use $M$-estimating functions to construct suitable summary statistics…
The synthesis model for the cosmic X-ray background (XRB) -based on the integrated emission of Active Galactic Nuclei (AGNs)- is complemented with new observational results. We adopt the most recent estimates of the AGN X-ray luminosity…
High-energy photons from dark matter annihilation contribute to the cosmic gamma-ray background (CGB). Since dark matter particles are weakly interacting, annihilation can happen only in high density regions such as dark matter halos. The…
Inferring parameter distributions of complex industrial systems from noisy time series data requires methods to deal with the uncertainty of the underlying data and the used simulation model. Bayesian inference is well suited for these…
Generalized Bayesian inference (GBI) is an alternative inference framework motivated by robustness to modeling errors, where a specific loss function is used to link the model parameters with observed data, instead of the log-likelihood…
The field of gamma ray astronomy relies heavily on the statistical analysis of data. Because of the paucity of data, and the often large errors associated with detecting gamma rays, analysis and interpretation of the data require…
In the paper, we continually use the method of image stacking to study the origin of the extragalactic gamma-ray background (EGB) at GeV bands, and find that the Faint Images of the Radio Sky at Twenty centimeters (FIRST) sources undetected…
The deep X-ray surveys performed by the two major X-ray observatories on flight, Chandra and XMM, are being resolving the bulk of the cosmic X-ray background (XRB) in the 2-10 keV energy band, where the sky flux is dominated by…
Approximate Bayesian Computation (ABC) methods are used to approximate posterior distributions in models with unknown or computationally intractable likelihoods. Both the accuracy and computational efficiency of ABC depend on the choice of…
A functional approximation to implement Bayesian source separation analysis is introduced and applied to separation of the Cosmic Microwave Background (CMB) using WMAP data. The approximation allows for tractable full-sky map…
Approximate Bayesian computation methods are useful for generative models with intractable likelihoods. These methods are however sensitive to the dimension of the parameter space, requiring exponentially increasing resources as this…
The observation of the diffuse Galactic gamma ray flux is the most powerful tool to study cosmic rays in different regions of the Galaxy, because the energy and angular distributions of the photons encode information about the density and…