• ADVI

  • Referenced in 26 articles [sw34040]
  • Automatic Variational Inference in Stan. Variational inference is a scalable technique for approximate Bayesian inference ... Deriving variational inference algorithms requires tedious model-specific 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 38 articles [sw12890]
  • methods is provided, including exact and variational inference, Expectation Propagation, and Laplace’s method dealing...
  • RStan

  • Referenced in 58 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 145 articles [sw04356]
  • package quantreg: Quantile Regression. Estimation and inference methods for models of conditional quantiles: Linear ... nonlinear parametric and non-parametric (total variation penalized) models for conditional quantiles of a univariate...
  • rstan

  • Referenced in 28 articles [sw16103]
  • Markov Chain Monte Carlo, rough Bayesian inference via variational approximation, and (optionally penalized) maximum likelihood...
  • GPflow

  • Referenced in 13 articles [sw21518]
  • GPflow are that it uses variational inference as the primary approximation method, provides concise code...
  • Venture

  • Referenced in 9 articles [sw14670]
  • implement general-purpose inference strategies such as Metropolis-Hastings, Gibbs sampling, and blocked proposals based ... chain Monte Carlo and mean-field variational inference techniques...
  • 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...
  • VIBES

  • Referenced in 7 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...
  • FFJORD

  • Referenced in 6 articles [sw34244]
  • dimensional density estimation, image generation, and variational inference, achieving the state-of-the-art among...
  • BayesPy

  • Referenced in 3 articles [sw15438]
  • BayesPy: variational Bayesian inference in Python. BayesPy is an open-source 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...
  • fastSTRUCTURE

  • Referenced in 4 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...
  • Salmon

  • Referenced in 4 articles [sw31865]
  • alignments), and massively-parallel stochastic collapsed variational inference. The result is a versatile tool that...
  • Medlda

  • Referenced in 11 articles [sw11723]
  • likelihood-driven 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...
  • Blaise

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

  • Referenced in 1 article [sw37284]
  • Variational Inference for Shrinkage Priors: The R package vir. We present vir, an R package ... variational inference with shrinkage priors. Our package implements variational and stochastic variational algorithms for linear ... many applied analyses. We review variational inference and show how the derivation for a Gibbs ... least for a normal linear model, variational inference can lead to similar uncertainty quantification...
  • Vprop

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

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

  • Referenced in 1 article [sw27939]
  • inference. The supported inference algorithms include: Variational inference with programmable variational posteriors, various objectives...