相关论文: On the efficiency of parameter space exploration: …

Many inverse problems in nuclear fusion and high-energy astrophysics research, such as the optimization of tokamak reactor geometries or the inference of black hole parameters from interferometric images, necessitate high-dimensional…

KNN is one of the most popular classification methods, but it often fails to work well with inappropriate choice of distance metric or due to the presence of numerous class-irrelevant features. Linear feature transformation methods have…

Using a Markov chain Monte Carlo approach, we find the allowed parameter space of a MSSM model with seven free parameters. In this model universality conditions at the GUT scale are imposed on the gaugino sector. We require in particular…

Sequential Monte Carlo (SMC), or particle filtering, is widely used in nonlinear state-space systems, but its performance often suffers from poorly approximated proposal and state-transition distributions. This work introduces a…

Bayesian inference in deep neural networks is challenging due to the high-dimensional, strongly multi-modal parameter posterior density landscape. Markov chain Monte Carlo approaches asymptotically recover the true posterior but are…

In all but the most trivial optimization problems, the structure of the solutions exhibit complex interdependencies between the input parameters. Decades of research with stochastic search techniques has shown the benefit of explicitly…

We constrain the parameters of dynamical dark energy in the form of a classical scalar field with barotropic equation of state jointly with other cosmological parameters using various combined datasets including the CMB power spectra from…

This research article presents a new cosmological model formulated within the $f(R,G,T)$ framework, focusing on the observational signatures and parameter constraints of the model. The Markov Chain Monte Carlo (MCMC) technique is employed…

In this letter, we propose an extension of the scotogenic model where singlet Majorana particle can be dark matter (DM) without the need of a highly suppressed scalar coupling of the order $O(10^{-10})$. For that, the SM is extended with…

We study the complementarity between dark matter experiments (direct detection and indirect detections) and accelerator facilities (the CERN LHC and a $\sqrt{s}= 1$ TeV $e^+e^-$ Linear Collider) in the framework of the constrained Minimal…

High-dimensional distributions, especially those with heavy tails, are notoriously difficult for off-the-shelf MCMC samplers: the combination of unbounded state spaces, diminishing gradient information, and local moves results in…

Markov chain Monte Carlo (MCMC) algorithms provide a very general recipe for estimating properties of complicated distributions. While their use has become commonplace and there is a large literature on MCMC theory and practice, MCMC users…

We propose a new computationally efficient sampling scheme for Bayesian inference involving high dimensional probability distributions. Our method maps the original parameter space into a low-dimensional latent space, explores the latent…

A phenomenological extension of the well-known brane-world cosmology of Dvali, Gabadadze and Porrati (eDGP) has recently been proposed. In this model, a cosmological-constant-like term is explicitly present as a non-vanishing tension sigma…

Simulations often involve the use of model parameters which are unknown or uncertain. For this reason, simulation experiments are often repeated for multiple combinations of parameter values, often iterating through parameter values lying…

We reexamine the minimal Singlet + Triplet Scotogenic Model, where dark matter is the mediator of neutrino mass generation. We assume it to be a scalar WIMP, whose stability follows from the same $\mathbb{Z} _{2}$ symmetry that leads to the…

As it has become common to use many computer cores in routine applications, finding good ways to parallelize popular algorithms has become increasingly important. In this paper, we present a parallelization scheme for Markov chain Monte…

Decaying Dark Energy models modify the background evolution of the most common observables, such as the Hubble function, the luminosity distance and the Cosmic Microwave Background temperature-redshift scaling relation. We use the most…

A grand challenge of the 21st century cosmology is to accurately estimate the cosmological parameters of our Universe. A major approach to estimating the cosmological parameters is to use the large-scale matter distribution of the Universe.…

Quantifying the uncertainty in model parameters and output is a critical component in model-driven decision support systems for groundwater management. This paper presents a novel algorithmic approach which fuses Markov Chain Monte Carlo…