1.7k citations · 1.9k across the 7 of their papers we have counts for
8 papers
Delayed rejection Hamiltonian Monte Carlo for sampling multiscale distributions
Chirag Modi, Alex Barnett, Bob Carpenter
The efficiency of Hamiltonian Monte Carlo (HMC) can suffer when sampling a distribution with a wide range of length scales, because the small step sizes needed for stability in hig…
Pathfinder: Parallel quasi-Newton variational inference
Lu Zhang, Bob Carpenter, Andrew Gelman +1
We propose Pathfinder, a variational method for approximately sampling from differentiable log densities. Starting from a random initialization, Pathfinder locates normal approxima…
Bayesian Workflow
Andrew Gelman, Aki Vehtari, Daniel Simpson +7
The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory. Probabilis…
Rank-normalization, folding, and localization: An improved for assessing convergence of MCMC
Aki Vehtari, Andrew Gelman, Daniel Simpson +2
Markov chain Monte Carlo is a key computational tool in Bayesian statistics, but it can be challenging to monitor the convergence of an iterative stochastic algorithm. In this pape…
Bayesian aggregation of average data: An application in drug development
Sebastian Weber, Andrew Gelman, Daniel Lee +3
Throughout the different phases of a drug development program, randomized trials are used to establish the tolerability, safety, and efficacy of a candidate drug. At each stage one…
Fitting Bayesian item response models in Stata and Stan
Robert L. Grant, Daniel C. Furr, Bob Carpenter +1
Stata users have access to two easy-to-use implementations of Bayesian inference: Stata's native {\tt bayesmh} function and StataStan, which calls the general Bayesian engine Stan.…