Amortized variational inference (A-VI) is a method for approximating the...
The hierarchical prior used in Latent Gaussian models (LGMs) induces a
p...
When factorized approximations are used for variational inference (VI), ...
Hamiltonian Monte Carlo (HMC) is a widely used sampler for continuous
pr...
Derivative-based algorithms are ubiquitous in statistics, machine learni...
When using Markov chain Monte Carlo (MCMC) algorithms, we can increase t...
Ising and Potts models are an important class of discrete probability
di...
We describe a class of algorithms for evaluating posterior moments of ce...
Stan is an open-source probabilistic programing language, primarily desi...
The Bayesian approach to data analysis provides a powerful way to handle...
This tutorial shows how to build, fit, and criticize disease transmissio...
Gaussian latent variable models are a key class of Bayesian hierarchical...
Latent Gaussian models are a popular class of hierarchical models with
a...
Gradient-based techniques are becoming increasingly critical in quantita...
Derivatives play a critical role in computational statistics, examples b...