Related papers: Das ist der HAMMER: Consistent new physics interpr…
The problem of sampling constrained continuous distributions has frequently appeared in many machine/statistical learning models. Many Monte Carlo Markov Chain (MCMC) sampling methods have been adapted to handle different types of…
Monte Carlo simulations of neutronic systems are computationally intensive and demand significant memory resources for high-fidelity modeling. Compressed sensing enables accurate reconstruction of signals from significantly fewer samples…
Dynamically rescaled Hamiltonian Monte Carlo (DRHMC) is introduced as a computationally fast and easily implemented method for performing full Bayesian analysis in hierarchical statistical models. The method relies on introducing a modified…
Equilibrium systems evolve according to Detailed Balance (DB). This principe guided development of the Monte-Carlo sampling techniques, of which Metropolis-Hastings (MH) algorithm is the famous representative. It is also known that DB is…
Wave-function Monte Carlo methods are an important tool for simulating quantum systems, but the standard method cannot be used to simulate decoherence in continuously measured systems. Here we present a new Monte Carlo method for such…
At the present time, there are hints of new physics (NP) in several observables involving $b \to c \ell^- {\bar\nu}_\ell$ decays. In this talk, I describe measurable angular distributions for ${\bar B} \to D^* \mu^- {\bar\nu}_\mu$ and…
Markov chain Monte Carlo (MCMC) algorithms offer various strategies for sampling; the Hamiltonian Monte Carlo (HMC) family of samplers are MCMC algorithms which often exhibit improved mixing properties. The recently introduced magnetic HMC,…
Quantitative theory of interbilayer interactions is essential to interpret x-ray scattering data and to elucidate these interactions for biologically relevant systems. For this purpose Monte Carlo simulations have been performed to obtain…
We present a scalable Bayesian framework for the analysis of confocal fluorescence spectroscopy data, addressing key limitations in traditional fluorescence correlation spectroscopy methods. Our framework captures molecular motion,…
The Broad Histogram Method (BHM) allows one to determine the energy degeneracy g(E), i.e. the energy spectrum of a given system, from the knowledge of the microcanonical averages <Nup(E)> and <Ndn(E)> of two macroscopic quantities Nup and…
We calculate the hadronic tensor for inclusive semileptonic B decay to O(alpha_s). This allows O(alpha_s Lambda_{QCD}/mb) corrections to hadronic invariant mass observables to be directly evaluated with experimentally required cuts on phase…
Monte Carlo event generators are an essential tool for data analysis in collider physics. To include subleading quantum corrections, these generators often need to produce negative weight events, which leads to statistical dilution of the…
Recent experimental results of ${\cal R}(D^{(*)})$ deviate from the standard model (SM) by $3.1\sigma$, suggesting a new physics (NP) that affects the $b\to c \tau \bar\nu_\tau$ transition. Motivated by this, we investigate the possible NP…
Experimental checks of the second row unitarity of the Cabibbo-Kobayashi-Maskawa (CKM) matrix involve extractions of the matrix element $V_{cd}$, which may be obtained from semileptonic decay rates of $D$ to $\pi$. These decay rates are…
Markov chain Monte Carlo (MCMC) methods have existed for a long time and the field is well-explored. The purpose of MCMC methods is to approximate a distribution through repeated sampling; most MCMC algorithms exhibit asymptotically optimal…
A popular regularized (shrinkage) covariance estimator is the shrinkage sample covariance matrix (SCM) which shares the same set of eigenvectors as the SCM but shrinks its eigenvalues toward its grand mean. In this paper, a more general…
The Belle Collaboration has measured the complete set of angular coefficient functions for the decays ${\bar B} \to D^*\,(D\,\pi)\,\ell\,{\bar \nu}_{\ell}$, where $\ell = e,\,\mu$, in four bins of the variable $w={m_B^2+m_{D^*}^2-q^2 \over…
In this work, we revisit the possible new physics (NP) solutions by analyzing the observables associated with $B\to D^{(\ast)}\tau\bar{\nu}_{\tau}$ decays. To explore the structure of new physics, the form factors of $B\to D^{(\ast)}$…
In this article, we describe a {\tt R} package for sampling from an empirical likelihood-based posterior using a Hamiltonian Monte Carlo method. Empirical likelihood-based methodologies have been used in Bayesian modeling of many problems…
High-throughput characterization often requires estimating parameters and model dimension from experimental data of limited quantity and quality. Such data may result in an ill-posed inverse problem, where multiple sets of parameters and…