Related papers: Applications of emulation and Bayesian methods in …
Bayesian inference provides a principled way of estimating the parameters of a stochastic process that is observed discretely in time. The overdamped Brownian motion of a particle confined in an optical trap is generally modelled by the…
Joint modeling of multiview graphs with a common set of nodes between views and auxiliary predictors is an essential, yet less explored, area in statistical methodology. Traditional approaches often treat graphs in different views as…
Mixture models are widely used in Bayesian statistics and machine learning, in particular in computational biology, natural language processing and many other fields. Variational inference, a technique for approximating intractable…
An unfolding method, based on Bayes theorem is presented to obtain true event-by-event net-charge multiplicity distribution from a corresponding measured distribution, which is subjected to detector artifacts. The unfolding is demonstrated…
We present a Bayesian methodology for infinite as well as finite dimensional parameter identification for partial differential equation models. The Bayesian framework provides a rigorous mathematical framework for incorporating prior…
Heavy quarks offer an invaluable hard probe of the droplets of quark gluon plasma (QGP) formed in heavy ion collisions at the LHC and RHIC. Given their large mass, they are predominantly produced in hard scattering processes at the earliest…
In model development, model calibration and validation play complementary roles toward learning reliable models. In this article, we expand the Bayesian Validation Metric framework to a general calibration and validation framework by…
Interpreting high-energy, astrophysical phenomena, such as supernova explosions or neutron-star collisions, requires a robust understanding of matter at supranuclear densities. However, our knowledge about dense matter explored in the cores…
To extract bulk QGP properties, we perform a joint Bayesian calibration of bulk-medium parameters using low-$\pt$ bulk and high-$\pt$ tomography within a common medium evolution. Low-$\pt$ observables are computed with…
We present a model for radiative energy loss of heavy quarks in quark gluon plasma which incorporates coherence effects. We then study its consequences on the radiation spectra as well as on the nuclear modification factor of open heavy…
A phenomenological analysis of the experimental measurements of transverse momentum spectra of identified charged hadrons and strange hyperons in Pb-Pb and Xe-Xe collisions at the LHC is presented. The analysis is based on the relativistic…
A simple geometrical model (often quoted in literature as the Glauber model) of heavy ion collisions is recapitulated. It is shown that the transverse energy distribution of heavy ion collisions follow the geometry of the collision. An…
Measurements of jet substructure in ultra-relativistic heavy-ion collisions indicate that interactions with the quark-gluon plasma quench the jet showering process. Modern data-driven methods have shown promise in probing these…
Jets produced in high-energy heavy-ion collisions are modified compared to those in proton-proton collisions due to their interaction with the deconfined, strongly-coupled quark-gluon plasma (QGP). In this work, we employ machine learning…
A recent development in finite temperature field theory, the so-called Braaten-Pisarski method, and its application to properties of a quark-gluon plasma, possibly formed in relativistic heavy ion collisions, are reviewed. In particular…
We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be…
The quest for precision in parameter estimation is a fundamental task in different scientific areas. The relevance of this problem thus provided the motivation to develop methods for the application of quantum resources to estimation…
A predictive Bayesian model selection approach is presented to discriminate coupled models used to predict an unobserved quantity of interest (QoI). The need for accurate predictions arises in a variety of critical applications such as…
Dynamical models based on relativistic fluid dynamics provide a powerful tool to extract the properties of the strongly-coupled quark-gluon plasma (QGP) produced by ultrarelativistic nuclear collisions. The largest source of uncertainty in…
The article considers a way to compare large bulks of experimental data with theoretical calculations, in which the quality of theoretical models is clearly demonstrated graphically. The main idea of the method consists in grouping physical…