Abstract—In this work we discuss gpustats, a new Python library for assist-ing in "big data " statistical computing applications, particularly Monte Carlo-based inference algorithms. The library provides a general code generation / metaprogramming framework for easily implementing discrete and continuous probability density functions and random variable samplers. These functions can be utilized to achieve more than 100x speedup over their CPU equivalents. We demonstrate their use in an Bayesian MCMC application and discuss avenues for future work
This is a short, practical guide that allows data scientists to understand the concepts of Graphical...
PySSM is a Python package that has been developed for the analysis of time series using linear Gauss...
Computational science is the application of computing technology to evaluate mathematical models in ...
This article describes advances in statistical computation for large-scale data analy-sis in structu...
ABCpy is a highly modular, scientific library for Approximate Bayesian Computation (ABC) written in ...
We describe the integration of Bayesian non-parametric mixture models, massively parallel computing ...
Probabilistic programming allows for automatic Bayesian inference on user-defined probabilistic mode...
ABCpy is a highly modular scientific library for approximate Bayesian computation (ABC) written in P...
Markov chain Monte Carlo (MCMC) estimation provides a solution to the complex integration problems t...
Motivation: The growing field of systems biology has driven demand for flexible tools to model and s...
We consider the problem of Bayesian inference in the family of probabilistic models implicitly defin...
Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions o...
International audienceThis paper presents the aGrUM framework, a LGPL C++ library providing state-of...
Abstract—Probabilistic Graphical Models (PGM) is a technique of compactly representing a joint distr...
We introduce the first, general purpose, slice sampling inference engine for probabilistic programs....
This is a short, practical guide that allows data scientists to understand the concepts of Graphical...
PySSM is a Python package that has been developed for the analysis of time series using linear Gauss...
Computational science is the application of computing technology to evaluate mathematical models in ...
This article describes advances in statistical computation for large-scale data analy-sis in structu...
ABCpy is a highly modular, scientific library for Approximate Bayesian Computation (ABC) written in ...
We describe the integration of Bayesian non-parametric mixture models, massively parallel computing ...
Probabilistic programming allows for automatic Bayesian inference on user-defined probabilistic mode...
ABCpy is a highly modular scientific library for approximate Bayesian computation (ABC) written in P...
Markov chain Monte Carlo (MCMC) estimation provides a solution to the complex integration problems t...
Motivation: The growing field of systems biology has driven demand for flexible tools to model and s...
We consider the problem of Bayesian inference in the family of probabilistic models implicitly defin...
Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions o...
International audienceThis paper presents the aGrUM framework, a LGPL C++ library providing state-of...
Abstract—Probabilistic Graphical Models (PGM) is a technique of compactly representing a joint distr...
We introduce the first, general purpose, slice sampling inference engine for probabilistic programs....
This is a short, practical guide that allows data scientists to understand the concepts of Graphical...
PySSM is a Python package that has been developed for the analysis of time series using linear Gauss...
Computational science is the application of computing technology to evaluate mathematical models in ...