Add to ORKGVariational Bayes is a popular method for approximate inference but its derivation can be cumbersome. To simplify the process, we give a 3-step recipe to identify the posterior form by explicitly looking for linearity with respect to expectations of well-known distributions. We can then directly write the update by simply ``reading-off'' the terms in front of those expectations. The recipe makes the derivation easier, faster, shorter, and more general
<p>One of the core problems of modern statistics is to approximate difficult-to-compute probability ...
Bayesian inference has become increasingly important in statistical machine learning. Exact Bayesian...
In this work, a framework to boost the efficiency of Bayesian inference in probabilistic models is i...
Variational Bayes is a popular method for approximate inference but its derivation can be cumbersome...
We develop strategies for mean field variational Bayes approximate inference for Bayesian hierarchic...
This dissertation is devoted to studying a fast and analytic approximation method, called the variat...
We advocate an optimization-centric view of Bayesian inference. Our inspiration is the representatio...
<p>Variational Bayes (VB) is rapidly becoming a popular tool for Bayesian inference in statistical m...
We develop a fast and accurate approach to approximate posterior distributions in the Bayesian empir...
We formulate natural gradient variational inference (VI), expectation propagation (EP), and posterio...
The Bayesian framework for machine learning allows for the incorporation of prior knowledge in a coh...
In this work, a framework to boost the efficiency of Bayesian inference in probabilistic models is i...
Variational inference is an optimization-based method for approximating the posterior distribution o...
Computation of the marginal likelihood from a simulated posterior distribution is central to Bayesia...
Recent advances in stochastic gradient varia-tional inference have made it possible to perform varia...
<p>One of the core problems of modern statistics is to approximate difficult-to-compute probability ...
Bayesian inference has become increasingly important in statistical machine learning. Exact Bayesian...
In this work, a framework to boost the efficiency of Bayesian inference in probabilistic models is i...
Variational Bayes is a popular method for approximate inference but its derivation can be cumbersome...
We develop strategies for mean field variational Bayes approximate inference for Bayesian hierarchic...
This dissertation is devoted to studying a fast and analytic approximation method, called the variat...
We advocate an optimization-centric view of Bayesian inference. Our inspiration is the representatio...
<p>Variational Bayes (VB) is rapidly becoming a popular tool for Bayesian inference in statistical m...
We develop a fast and accurate approach to approximate posterior distributions in the Bayesian empir...
We formulate natural gradient variational inference (VI), expectation propagation (EP), and posterio...
The Bayesian framework for machine learning allows for the incorporation of prior knowledge in a coh...
In this work, a framework to boost the efficiency of Bayesian inference in probabilistic models is i...
Variational inference is an optimization-based method for approximating the posterior distribution o...
Computation of the marginal likelihood from a simulated posterior distribution is central to Bayesia...
Recent advances in stochastic gradient varia-tional inference have made it possible to perform varia...
<p>One of the core problems of modern statistics is to approximate difficult-to-compute probability ...
Bayesian inference has become increasingly important in statistical machine learning. Exact Bayesian...
In this work, a framework to boost the efficiency of Bayesian inference in probabilistic models is i...