Publication date
May 2017
DOI Publisher
Informa UK Limited Journal
Journal of Computational and Graphical StatisticsAbstract
10.1080/10618600.2017.1330205Journal of Computational and Graphical Statistics264873-88
10.1080/10618600.2017.1330205Journal of Computational and Graphical Statistics264873-88
Variational inference is one of the tools that now lies at the heart of the modern data analysis lif...
Abstract: Reliable tools for reduction of dimensionality are needed for data processing in many area...
How can we perform efficient inference and learning in directed probabilistic models, in the presenc...
<p>Variational Bayes (VB) is rapidly becoming a popular tool for Bayesian inference in statistical m...
10.1080/10618600.2017.1302882Journal of Computational and Graphical Statistics2711-1
The following full text is a publisher's version. For additional information about this publica...
10.1080/10618600.2015.1012293Journal of Computational and Graphical Statistics252626-64
10.1080/10618600.2020.1740714Journal of Computational and Graphical Statistics2904910-92
We develop a fast and accurate approach to approximate posterior distributions in the Bayesian empir...
We present a novel method for approximate inference. Using some of the constructs from expectation p...
We present a novel method for approximate inference. Using some of the constructs from expectation p...
This tutorial describes the mean-field variational Bayesian approximation to inference in graphical ...
This chapter surveys computational methods for posterior inference with intractable likelihoods, tha...
Approximate Bayesian computation techniques, also called likelihood-free methods, are one of the mos...
Synthetic likelihood is an attractive approach to likelihood-free inference when an approximately Ga...
Variational inference is one of the tools that now lies at the heart of the modern data analysis lif...
Abstract: Reliable tools for reduction of dimensionality are needed for data processing in many area...
How can we perform efficient inference and learning in directed probabilistic models, in the presenc...
<p>Variational Bayes (VB) is rapidly becoming a popular tool for Bayesian inference in statistical m...
10.1080/10618600.2017.1302882Journal of Computational and Graphical Statistics2711-1
The following full text is a publisher's version. For additional information about this publica...
10.1080/10618600.2015.1012293Journal of Computational and Graphical Statistics252626-64
10.1080/10618600.2020.1740714Journal of Computational and Graphical Statistics2904910-92
We develop a fast and accurate approach to approximate posterior distributions in the Bayesian empir...
We present a novel method for approximate inference. Using some of the constructs from expectation p...
We present a novel method for approximate inference. Using some of the constructs from expectation p...
This tutorial describes the mean-field variational Bayesian approximation to inference in graphical ...
This chapter surveys computational methods for posterior inference with intractable likelihoods, tha...
Approximate Bayesian computation techniques, also called likelihood-free methods, are one of the mos...
Synthetic likelihood is an attractive approach to likelihood-free inference when an approximately Ga...
Variational inference is one of the tools that now lies at the heart of the modern data analysis lif...
Abstract: Reliable tools for reduction of dimensionality are needed for data processing in many area...
How can we perform efficient inference and learning in directed probabilistic models, in the presenc...
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