Hybrid Monte Carlo

TL;DR: Bayesian neural networks trained using Markov chain Monte Carlo (MCMC) and genetic programming in binary space within Metropolis framework is proposed and is observed to give better results than classical approach.

TL;DR: Application of graph-GPA to a joint analysis of GWAS datasets for 12 phenotypes shows that graph- GPA improves statistical power to identify risk variants compared to statistical methods based on smaller number ofGWAS datasets.

TL;DR: This work gives Statistical PCF a sampling-style operational semantics, and uses a stochastic form of symbolic execution to reason about almost everywhere differentiability, which results in almost surely terminating deterministic SPCF programs with real parameters denote functions that are almost everything differentiable.

TL;DR: In this article, a multi-category brand choice model is proposed based on the conceptualization that the intrinsic utility for a brand is a function of underlying attributes or characteristics, some of which are common across categories.

TL;DR: It is argued that the new proposal always represents a significant improvement over a Langevin simulation, and may even improve over the microcanonical method, in which case only a trivial code modification is required.

TL;DR: In this paper, it was shown that the success of acceleration for abelian gauge field dynamics need not depend on any choice of gauge and proposed a particular scheme for acceleration in non-abelian theories which is also gauge independent.

TL;DR: Two algorithms for the numerical simulation of SU(3) lattice gauge theory with dynamical quarks are discussed, based on the hybrid stochastic method of Duane and Kogut, which allow the simulation of arbitrary numbers of quarks.

TL;DR: Fourier techniques that greatly accelerate simulations on large lattices and a new technique for including quark vacuum-polarization corrections that admits any number of flavors, odd or even, without the need for nested Monte Carlo calculations are introduced.