143 citations · 145 across the 3 of their papers we have counts for
5 papers
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…
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…
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…
A Review of automatic differentiation and its efficient implementation
Charles C. Margossian
Derivatives play a critical role in computational statistics, examples being Bayesian inference using Hamiltonian Monte Carlo sampling and the training of neural networks. Automati…