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

Many modern statistical applications involve inference for complicated stochastic models for which the likelihood function is difficult or even impossible to calculate, and hence conventional likelihood-based inferential echniques cannot be…

Approximate Bayesian Computation (ABC) enables statistical inference in simulator-based models whose likelihoods are difficult to calculate but easy to simulate from. ABC constructs a kernel-type approximation to the posterior distribution…

Approximate Bayesian computation (ABC) methods, which are applicable when the likelihood is difficult or impossible to calculate, are an active topic of current research. Most current ABC algorithms directly approximate the posterior…

Gamma-ray searches for new physics such as dark matter are often driven by investigating the composition of the extragalactic gamma-ray background (EGB). Classic approaches to EGB decomposition manifest in resolving individual point sources…

Approximate Bayesian Computation (ABC) is a method to obtain a posterior distribution without a likelihood function, using simulations and a set of distance metrics. For that reason, it has recently been gaining popularity as an analysis…

Approximate Bayesian computation (ABC) has become an essential part of the Bayesian toolbox for addressing problems in which the likelihood is prohibitively expensive or entirely unknown, making it intractable. ABC defines a…

The origin of the cosmic gamma-ray background (CGB) is a longstanding mystery in high-energy astrophysics. Possible candidates include ordinary astrophysical objects such as unresolved blazars, as well as more exotic processes such as dark…

The diffuse extragalactic gamma-ray background (EGRB) above 100 MeV encodes unique information about high-energy processes in the universe. Numerous sources for the EGRB have been proposed, but the two systems which are certain to make some…

Decaying or annihilating dark matter particles could be detected through gamma-ray emission from the species they decay or annihilate into. This is usually done by modelling the flux from specific dark matter-rich objects such as the Milky…

In this work we develop a new propagation model for the Galactic cosmic rays based on the GALPROP code, including contributions from dark matter annihilation. The model predicts compatible Galactic diffuse $\gamma$ ray spectra with EGRET…

Approximate Bayesian computation (ABC) have become a essential tool for the analysis of complex stochastic models. Earlier, Grelaud et al. (2009) advocated the use of ABC for Bayesian model choice in the specific case of Gibbs random…

Several classes of astrophysical sources contribute to the approximately isotropic gamma-ray background measured by the Fermi Gamma-Ray Space Telescope. In this paper, we use Fermi's catalog of gamma-ray sources (along with corresponding…

We are living in the big data era, as current technologies and networks allow for the easy and routine collection of data sets in different disciplines. Bayesian Statistics offers a flexible modeling approach which is attractive for…

Approximate Bayesian Computation (ABC) enables parameter inference for complex physical systems in cases where the true likelihood function is unknown, unavailable, or computationally too expensive. It relies on the forward simulation of…

Approximate Bayesian Computation (ABC for short) is a family of computational techniques which offer an almost automated solution in situations where evaluation of the posterior likelihood is computationally prohibitive, or whenever…

The one-point function (i.e., the isotropic flux distribution) is a complementary method to (anisotropic) two-point correlations in searches for a gamma-ray dark matter annihilation signature. Using analytical models of structure formation…

A computationally simple approach to inference in state space models is proposed, using approximate Bayesian computation (ABC). ABC avoids evaluation of an intractable likelihood by matching summary statistics for the observed data with…

Dark matter (DM) annihilation could in principle contribute to the diffuse cosmic gamma-ray back- ground (CGB). While with standard assumptions for cosmological and particle physics parameters this contribution is expected to be rather…

Approximate Bayesian Computation (ABC) is a popular inference method when likelihoods are hard to come by. Practical bottlenecks of ABC applications include selecting statistics that summarize the data without losing too much information or…

With larger data at their disposal, scientists are emboldened to tackle complex questions that require sophisticated statistical models. It is not unusual for the latter to have likelihood functions that elude analytical formulations. Even…