331 citations · 504 across the 25 of their papers we have counts for
10 papers · 1 filter
Large-scale empirical tuning and comparison of default optimizers for variational inference
Trevor Campbell, Jonathan H. Huggins, Kyurae Kim +1
Black-box variational inference (BBVI) is a methodology for posterior approximation that relies on stochastic optimization. In practice, the stochastic optimizers underpinning BBVI…
For how many iterations should we run Markov chain Monte Carlo?
Charles C. Margossian, Andrew Gelman
Standard Markov chain Monte Carlo (MCMC) admits three fundamental control parameters: the number of chains, the length of the warmup phase, and the length of the sampling phase. Th…
General adjoint-differentiated Laplace approximation
Charles C. Margossian
The hierarchical prior used in Latent Gaussian models (LGMs) induces a posterior geometry prone to frustrate inference algorithms. Marginalizing out the latent Gaussian variable us…
Adaptive Tuning for Metropolis Adjusted Langevin Trajectories
Lionel Riou-Durand, Pavel Sountsov, Jure Vogrinc +2
Hamiltonian Monte Carlo (HMC) is a widely used sampler for continuous probability distributions. In many cases, the underlying Hamiltonian dynamics exhibit a phenomenon of resonanc…
Efficient Automatic Differentiation of Implicit Functions
Charles C. Margossian, Michael Betancourt
Derivative-based algorithms are ubiquitous in statistics, machine learning, and applied mathematics. Automatic differentiation offers an algorithmic way to efficiently evaluate the…
Fast methods for posterior inference of two-group normal-normal models
Philip Greengard, Jeremy Hoskins, Charles C. Margossian +2
We describe a class of algorithms for evaluating posterior moments of certain Bayesian linear regression models with a normal likelihood and a normal prior on the regression coeffi…
Efficient Sampling for Ising and Potts Models using Auxiliary Gaussian Variables
Charles C. Margossian, Chenyang Zhong, Sumit Mukherjee
Ising and Potts models are an important class of discrete probability distributions which originated from statistical physics and since then have found applications in several disc…
Bayesian workflow for disease transmission modeling in Stan
Léo Grinsztajn, Elizaveta Semenova, Charles C. Margossian +1
This tutorial shows how to build, fit, and criticize disease transmission models in Stan, and should be useful to researchers interested in modeling the SARS-CoV-2 pandemic and oth…
Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond
Charles C. Margossian, Aki Vehtari, Daniel Simpson +1
Gaussian latent variable models are a key class of Bayesian hierarchical models with applications in many fields. Performing Bayesian inference on such models can be challenging as…
The Discrete Adjoint Method: Efficient Derivatives for Functions of Discrete Sequences
Michael Betancourt, Charles C. Margossian, Vianey Leos-Barajas
Gradient-based techniques are becoming increasingly critical in quantitative fields, notably in statistics and computer science. The utility of these techniques, however, ultimatel…