
ADVI
 Referenced in 23 articles
[sw34040]
 Automatic Variational Inference in Stan. Variational inference is a scalable technique for approximate Bayesian inference ... Deriving variational inference algorithms requires tedious modelspecific calculations; this makes it difficult to automate ... propose an automatic variational inference algorithm, automatic differentiation variational inference (ADVI). The user only provides ... images. With ADVI we can use variational inference on any model we write in Stan...

GPML
 Referenced in 37 articles
[sw12890]
 methods is provided, including exact and variational inference, Expectation Propagation, and Laplace’s method dealing...

RStan
 Referenced in 50 articles
[sw13990]
 Markov Chain Monte Carlo, rough Bayesian inference via variational approximation, and (optionally penalized) maximum likelihood...

LDPC
 Referenced in 79 articles
[sw03321]
 codes, and to probabilistic inference methods used in other fields. Variations on LDPC and Turbo...

quantreg
 Referenced in 138 articles
[sw04356]
 package quantreg: Quantile Regression. Estimation and inference methods for models of conditional quantiles: Linear ... nonlinear parametric and nonparametric (total variation penalized) models for conditional quantiles of a univariate...

rstan
 Referenced in 21 articles
[sw16103]
 Markov Chain Monte Carlo, rough Bayesian inference via variational approximation, and (optionally penalized) maximum likelihood...

GPflow
 Referenced in 12 articles
[sw21518]
 GPflow are that it uses variational inference as the primary approximation method, provides concise code...

Venture
 Referenced in 8 articles
[sw14670]
 implement generalpurpose inference strategies such as MetropolisHastings, Gibbs sampling, and blocked proposals based ... chain Monte Carlo and meanfield variational inference techniques...

VIBES
 Referenced in 8 articles
[sw15439]
 software package which allows variational inference to be performed automatically on a Bayesian network ... Ph.D. as an implementation of my Variational Message Passing algorithm...

AMIDST
 Referenced in 5 articles
[sw21741]
 Scaling up Bayesian variational inference using distributed computing clusters. In this paper we present ... approach for scaling up Bayesian learning using variational methods by exploiting distributed computing clusters managed ... approach compares favorably to stochastic variational inference and streaming variational Bayes, two of the main...

Medlda
 Referenced in 11 articles
[sw11723]
 likelihooddriven objective functions for learning and inference, leaving the popular and potentially powerful ... side information is available. Efficient variational methods for posterior inference and parameter estimation are derived...

Salmon
 Referenced in 3 articles
[sw31865]
 alignments), and massivelyparallel stochastic collapsed variational inference. The result is a versatile tool that...

FFJORD
 Referenced in 3 articles
[sw34244]
 dimensional density estimation, image generation, and variational inference, achieving the stateoftheart among...

Blaise
 Referenced in 8 articles
[sw29867]
 processors and computing clusters, and inference schemes based on variational methods and message passing...

ZhuSuan
 Referenced in 2 articles
[sw27939]
 inference. The supported inference algorithms include: Variational inference with programmable variational posteriors, various objectives...

Vprop
 Referenced in 1 article
[sw22203]
 Vprop: Variational Inference using RMSprop. Many computationallyefficient methods for Bayesian deep learning rely ... propose Vprop, a method for Gaussian variational inference that can be implemented with two minor ... memory requirements of BlackBox Variational Inference by half. We derive Vprop using the conjugate ... computation variational inference method, and establish its connections to Newton’s method, naturalgradient methods...

fastSTRUCTURE
 Referenced in 2 articles
[sw25207]
 fastSTRUCTURE: variational inference of population structure in large SNP data sets. Tools for estimating population ... approximate inference of the model underlying the STRUCTURE program using a variational Bayesian framework. Variational ... advances in optimization theory to develop fast inference tools. In addition, we propose useful heuristic ... structure in the data. We test the variational algorithms on simulated data and illustrate using...

BayesPy
 Referenced in 1 article
[sw15438]
 BayesPy: variational Bayesian inference in Python. BayesPy is an opensource Python software package ... performing variational Bayesian inference. It is based on the variational message passing framework and supports ... removing the tedious task of implementing the variational Bayesian update equations, the user can construct ... methods such as stochastic and collapsed variational inference...

nflows
 Referenced in 2 articles
[sw35005]
 neural spline flows improve density estimation, variational inference, and generative modeling of images...

HapMix
 Referenced in 6 articles
[sw33847]
 perform such local ancestry inference based on finescale variation data. We show that HAPMIX ... methods, and we explore its utility for inferring ancestry, learning about ancestral populations, and inferring...