64 citations · 130 across the 26 of their papers we have counts for
5 papers · 1 filter
posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms
Måns Magnusson, Jakob Torgander, Paul-Christian Bürkner +3
The generality and robustness of inference algorithms is critical to the success of widely used probabilistic programming languages such as Stan, PyMC, Pyro, and Turing.jl. When de…
The ARR2 prior: flexible predictive prior definition for Bayesian auto-regressions
David Kohns, Noa Kallioinen, Yann McLatchie +1
We present the ARR2 prior, a joint prior over the auto-regressive components in Bayesian time-series models and their induced . Compared to other priors designed for times-ser…
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…
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…
Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data
Aki Vehtari, Andrew Gelman, Tuomas Sivula +7
A common divide-and-conquer approach for Bayesian computation with big data is to partition the data, perform local inference for each piece separately, and combine the results to…