research
∙
07/20/2023
Amortized Variational Inference: When and Why?
Amortized variational inference (A-VI) is a method for approximating the...
share
research
∙
06/26/2023
General adjoint-differentiated Laplace approximation
The hierarchical prior used in Latent Gaussian models (LGMs) induces a p...
research
∙
02/17/2023
The Shrinkage-Delinkage Trade-off: An Analysis of Factorized Gaussian Approximations for Variational Inference
When factorized approximations are used for variational inference (VI), ...
research
∙
10/21/2022
Adaptive Tuning for Metropolis Adjusted Langevin Trajectories
Hamiltonian Monte Carlo (HMC) is a widely used sampler for continuous pr...
research
∙
12/28/2021
Efficient Automatic Differentiation of Implicit Functions
Derivative-based algorithms are ubiquitous in statistics, machine learni...
research
∙
10/25/2021
Nested RÌ‚: Assessing Convergence for Markov chain Monte Carlo when using many short chains
When using Markov chain Monte Carlo (MCMC) algorithms, we can increase t...
research
∙
10/20/2021
Simulating Ising and Potts models at critical and cold temperatures using auxiliary Gaussian variables
Ising and Potts models are an important class of discrete probability di...
research
∙
10/06/2021
Fast methods for posterior inference of two-group normal-normal models
We describe a class of algorithms for evaluating posterior moments of ce...
research
∙
09/21/2021
Flexible and efficient Bayesian pharmacometrics modeling using Stan and Torsten, Part I
Stan is an open-source probabilistic programing language, primarily desi...
research
∙
11/03/2020
Bayesian Workflow
The Bayesian approach to data analysis provides a powerful way to handle...
research
∙
05/23/2020
Bayesian workflow for disease transmission modeling in Stan
This tutorial shows how to build, fit, and criticize disease transmissio...
research
∙
04/27/2020
Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation
Gaussian latent variable models are a key class of Bayesian hierarchical...
research
∙
04/27/2020
Hamiltonian Monte Carlo using an embedded Laplace approximation
Latent Gaussian models are a popular class of hierarchical models with a...
research
∙
02/02/2020
The Discrete Adjoint Method: Efficient Derivatives for Functions of Discrete Sequences
Gradient-based techniques are becoming increasingly critical in quantita...
research
∙
11/12/2018
