© 2013 John Wiley & Sons Ltd. We derive a variational inference procedure for approximate Bayesian inference in marginal longitudinal semiparametric regression. Fitting and inference is much faster than existing Markov chain Monte Carlo approaches. Numerical studies indicate that the new methodology is very accurate for the class of models under consideration
Fast variational approximate algorithms are developed for Bayesian semiparametric regression when th...
We develop a fast and accurate approach to approximate posterior distributions in the Bayesian empir...
A new method called “variational sampling ” is proposed to estimate integrals under probability dist...
Variational approximation methods are enjoying an increasing amount of development and use in statis...
Variational methods for approximate Bayesian inference provide fast, flexible, deterministic alterna...
Abstract. The article describe the model, derivation, and implementation of variational Bayesian inf...
We present a novel method for approximate inference. Using some of the constructs from expectation p...
<div><p>Variational approximations provide fast, deterministic alternatives to Markov Chain Monte Ca...
Variational methods are widely used for approximate posterior inference. However, their use is typic...
We present a novel method for approximate inference. Using some of the constructs from expectation p...
Recent advances in stochastic gradient variational inference have made it possi-ble to perform varia...
Recent advances in stochastic gradient varia-tional inference have made it possible to perform varia...
Variational inference is one of the tools that now lies at the heart of the modern data analysis lif...
Semiparametric regression offers a flexible framework for modeling non-linear relationships between ...
Recent advances in stochastic gradient variational inference have made it possible to perform variat...
Fast variational approximate algorithms are developed for Bayesian semiparametric regression when th...
We develop a fast and accurate approach to approximate posterior distributions in the Bayesian empir...
A new method called “variational sampling ” is proposed to estimate integrals under probability dist...
Variational approximation methods are enjoying an increasing amount of development and use in statis...
Variational methods for approximate Bayesian inference provide fast, flexible, deterministic alterna...
Abstract. The article describe the model, derivation, and implementation of variational Bayesian inf...
We present a novel method for approximate inference. Using some of the constructs from expectation p...
<div><p>Variational approximations provide fast, deterministic alternatives to Markov Chain Monte Ca...
Variational methods are widely used for approximate posterior inference. However, their use is typic...
We present a novel method for approximate inference. Using some of the constructs from expectation p...
Recent advances in stochastic gradient variational inference have made it possi-ble to perform varia...
Recent advances in stochastic gradient varia-tional inference have made it possible to perform varia...
Variational inference is one of the tools that now lies at the heart of the modern data analysis lif...
Semiparametric regression offers a flexible framework for modeling non-linear relationships between ...
Recent advances in stochastic gradient variational inference have made it possible to perform variat...
Fast variational approximate algorithms are developed for Bayesian semiparametric regression when th...
We develop a fast and accurate approach to approximate posterior distributions in the Bayesian empir...
A new method called “variational sampling ” is proposed to estimate integrals under probability dist...