Search Results for author:
Found 9 papers, 5 papers with code
GIST: Gibbs self-tuning for locally adaptive Hamiltonian Monte Carlo
We present a novel and flexible framework for localized tuning of Hamiltonian Monte Carlo samplers by sampling the algorithm's tuning parameters conditionally based on the position and momentum at each step.
Ensemble reweighting using Cryo-EM particles
Cryo-electron microscopy (cryo-EM) has recently become a premier method for obtaining high-resolution structures of biological macromolecules.
Delayed rejection Hamiltonian Monte Carlo for sampling multiscale distributions
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 high-curvature regions are inefficient elsewhere.
Pathfinder: Parallel quasi-Newton variational inference
Pathfinder returns draws from the approximation with the lowest estimated Kullback-Leibler (KL) divergence to the true posterior.
A Bayesian approach for extracting free energy profiles from cryo-electron microscopy experiments using a path collective variable
Cryo-electron microscopy (cryo-EM) extracts single-particle density projections of individual biomolecules.
Rank-normalization, folding, and localization: An improved $\widehat{R}$ for assessing convergence of MCMC
In this paper we show that the convergence diagnostic $\widehat{R}$ of Gelman and Rubin (1992) has serious flaws.
Computation Methodology
Comparing Bayesian Models of Annotation
We evaluate these models along four aspects: comparison to gold labels, predictive accuracy for new annotations, annotator characterization, and item difficulty, using four datasets with varying degrees of noise in the form of random (spammy) annotators.
The Stan Math Library: Reverse-Mode Automatic Differentiation in C++
As computational challenges in optimization and statistical inference grow ever harder, algorithms that utilize derivatives are becoming increasingly more important.
Mathematical Software G.1.0; G.1.3; G.1.4; F.2.1
The Benefits of a Model of Annotation
Standard agreement measures for interannotator reliability are neither necessary nor sufficient to ensure a high quality corpus.
