Add to ORKGThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (page 49).In this thesis, I look at multiple Variational Inference algorithm, transform Kalman Variational Bayes and Stochastic Variational Inference into streaming algorithms and try to identify if any of them work with non-stationary distributions. I conclude that Kalman Variational Bayes can do as good as any other algorithm for stationary distributions, and tracks non-stationary distrib...
Variational methods for approximate Bayesian inference provide fast, flexible, deterministic alterna...
Variational inference is a scalable technique for approximate Bayesian inference. Deriving variation...
Mean-field variational methods are widely used for approximate posterior inference in many probabili...
We propose a simple and effective variational inference algorithm based on stochastic optimi-sation ...
Variational inference is one of the tools that now lies at the heart of the modern data analysis lif...
<p>One of the core problems of modern statistics is to approximate difficult-to-compute probability ...
We present a truncation-free online variational inference algorithm for Bayesian nonparametric model...
The Variational Bayes (VB) approximation is applied in the context of Bayesian filtering, yielding a...
Bayesian nonparametric models are theoretically suitable to learn streaming data due to their comple...
This tutorial describes the mean-field variational Bayesian approximation to inference in graphical ...
In this paper the variational Bayesian approximation for partially observed continuous time stochast...
Variational inference is a popular alternative to Markov chain Monte Carlo methods that constructs ...
Variational Inference (VI) has become a popular technique to approximate difficult-to-compute poster...
In many modern data analysis problems, the available data is not static but, instead, comes in a str...
The central objective of this thesis is to develop new algorithms for inference in probabilistic gra...
Variational methods for approximate Bayesian inference provide fast, flexible, deterministic alterna...
Variational inference is a scalable technique for approximate Bayesian inference. Deriving variation...
Mean-field variational methods are widely used for approximate posterior inference in many probabili...
We propose a simple and effective variational inference algorithm based on stochastic optimi-sation ...
Variational inference is one of the tools that now lies at the heart of the modern data analysis lif...
<p>One of the core problems of modern statistics is to approximate difficult-to-compute probability ...
We present a truncation-free online variational inference algorithm for Bayesian nonparametric model...
The Variational Bayes (VB) approximation is applied in the context of Bayesian filtering, yielding a...
Bayesian nonparametric models are theoretically suitable to learn streaming data due to their comple...
This tutorial describes the mean-field variational Bayesian approximation to inference in graphical ...
In this paper the variational Bayesian approximation for partially observed continuous time stochast...
Variational inference is a popular alternative to Markov chain Monte Carlo methods that constructs ...
Variational Inference (VI) has become a popular technique to approximate difficult-to-compute poster...
In many modern data analysis problems, the available data is not static but, instead, comes in a str...
The central objective of this thesis is to develop new algorithms for inference in probabilistic gra...
Variational methods for approximate Bayesian inference provide fast, flexible, deterministic alterna...
Variational inference is a scalable technique for approximate Bayesian inference. Deriving variation...
Mean-field variational methods are widely used for approximate posterior inference in many probabili...