Search Results
ParaMonte: A high-performance serial/parallel Monte Carlo simulation library for C, C++, Fortran
ParaMonte (standing for Parallel Monte Carlo) is a serial and MPI/Coarray-parallelized library of Monte Carlo routines for sampling mathematical objective functions of arbitrary-dimensions, in particular, the posterior distributions of Bayesian models in data science, Machine Learning, and scientific inference.
Liesel: A Probabilistic Programming Framework for Developing Semi-Parametric Regression Models and Custom Bayesian Inference Algorithms
Liesel is a new probabilistic programming framework developed with the aim of supporting research on Bayesian inference based on Markov chain Monte Carlo (MCMC) simulations in general and semi-parametric regression specifications in particular.
Computation
Nonparametric Involutive Markov Chain Monte Carlo
A challenging problem in probabilistic programming is to develop inference algorithms that work for arbitrary programs in a universal probabilistic programming language (PPL).
EasyScan_HEP: a tool for connecting programs to scan the parameter space of physics models
We present an application, EasyScan_HEP, for connecting programs to scan the parameter space of High Energy Physics (HEP) models using various sampling algorithms.
High Energy Physics - Phenomenology Data Analysis, Statistics and Probability
Functional probabilistic programming for scalable Bayesian modelling
This paper introduces functional programming principles which can be used to develop an embedded probabilistic programming language.
Computation
Bayesian Safety Surveillance with Adaptive Bias Correction
Post-market safety surveillance is an integral part of mass vaccination programs.
Methodology Applications
pexm: a JAGS module for applications involving the piecewise exponential distribution
In this study, we present a new module built for users interested in a programming language similar to BUGS to fit a Bayesian model based on the piecewise exponential (PE) distribution.
Computation 62-XX, 62-04 G.3
Boltzmann sampling with quantum annealers via fast Stein correction
Despite the attempts to apply a quantum annealer to Boltzmann sampling, it is still impossible to perform accurate sampling at arbitrary temperatures.
Statistical Mechanics Quantum Physics
