Related papers: Das ist der HAMMER: Consistent new physics interpr…
Motivated by the persistent anomalies reported in the $b\to c\tau\bar{\nu}$ data, we perform a general model-independent analysis of these transitions, in the presence of light right-handed neutrinos. We adopt an effective field theory…
Sampling from high dimensional distributions is a computational bottleneck in many scientific applications. Hamiltonian Monte Carlo (HMC), and in particular the No-U-Turn Sampler (NUTS), are widely used, yet they struggle on problems with a…
Riemannian manifold Hamiltonian Monte Carlo (RMHMC) is a sampling algorithm that seeks to adapt proposals to the local geometry of the posterior distribution. The specific form of the Hamiltonian used in RMHMC necessitates {\it…
Hamiltonian Monte Carlo (HMC) is an efficient method of simulating smooth distributions and has motivated the widely used No-U-turn Sampler (NUTS) and software Stan. We build on NUTS and the technique of "unbiased sampling" to design HMC…
Hamiltonian Monte Carlo (HMC) samples efficiently from high-dimensional posterior distributions with proposed parameter draws obtained by iterating on a discretized version of the Hamiltonian dynamics. The iterations make HMC…
The Hamiltonian Monte Carlo (HMC) method has been recognized as a powerful sampling tool in computational statistics. We show that performance of HMC can be significantly improved by incorporating importance sampling and an irreversible…
We introduce a novel method for extracting a fragmentation model directly from experimental data without requiring an explicit parametric form, called Histories and Observables for Monte-Carlo Event Reweighting (HOMER), consisting of three…
Current experimental information on the charm meson decay observables in which the $c\to s\ell\nu_\ell$ transitions occur is well compatible with the Standard Model predictions. Recent precise lattice calculations of the $D_s$ meson decay…
Measurements of the branching fractions of the semileptonic decays $B\to D^{(*)}\tau\bar\nu_\tau$ and $B_c\to J/\psi\tau\bar\nu_\tau$ systematically exceed the Standard Model (SM) predictions, pointing to possible signals of new physics…
Recent anomalies in decays induced by $b\to c \ell^- \bar\nu_\ell$ transitions raise the question about such phenomena in the $D$ decays induced by $c\to (s,d)\ell^+\nu_\ell$ transitions. In the experimental side, current measurements on…
The first part of this talk reviews recent developments in flavor physics that can be made without detailed understanding of hadronic physics, driven by the data. The error of sin(2beta) has shrunk below 5%, and the measurements of alpha…
In this work we present a new and efficient Bayesian method for nonlinear three dimensional large scale structure inference. We employ a Hamiltonian Monte Carlo (HMC) sampler to obtain samples from a multivariate highly non-Gaussian…
Sampling from hierarchical Bayesian models is often difficult for MCMC methods, because of the strong correlations between the model parameters and the hyperparameters. Recent Riemannian manifold Hamiltonian Monte Carlo (RMHMC) methods have…
We study the semileptonic decays of the $B_c$ meson into final charmonium states within the standard model and beyond. The relevant hadronic transition form factors are calculated in the framework of the covariant confined quark model…
I review the current status of measurements involving semi-tauonic $B$ meson decay at the $B$-factories. I briefly discuss the experimental methods and highlight the importance of background contributions especially from poorly understood…
Recent experimental data on several observables in semileptonic $B$-meson decays are found to be in tension with the corresponding Standard Model predictions. Most of these deviations are related to $b \to c$ and $b \to s$ flavour changing…
The Hamiltonian Monte Carlo (HMC) method allows sampling from continuous densities. Favorable scaling with dimension has led to wide adoption of HMC by the statistics community. Modern auto-differentiating software should allow more…
Bayesian max-margin models have shown superiority in various practical applications, such as text categorization, collaborative prediction, social network link prediction and crowdsourcing, and they conjoin the flexibility of Bayesian…
Sequential Monte Carlo (SMC) samplers form an attractive alternative to MCMC for Bayesian computation. However, their performance depends strongly on the Markov kernels used to rejuvenate particles. We discuss how to calibrate automatically…
In this paper we address the widely-experienced difficulty in tuning Hamiltonian-based Monte Carlo samplers. We develop an algorithm that allows for the adaptation of Hamiltonian and Riemann manifold Hamiltonian Monte Carlo samplers using…