Add to ORKGRecent advances in stochastic gradient variational inference have made it possible to perform variational Bayesian inference with posterior approximations containing auxiliary random variables. This enables us to explore a new synthesis of variational inference and Monte Carlo methods where we incorporate one or more steps of MCMC into our variational approximation. We describe the theoretical foundations that make this possible and show some promising first results
The Markov Chain Monte Carlo (MCMC) technique provides a means to generate a random sequence of mode...
A key design constraint when implementing Monte Carlo and variational inference algorithms is that i...
Variational approximation methods are enjoying an increasing amount of development and use in statis...
Recent advances in stochastic gradient variational inference have made it possible to perform variat...
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 methods for approximate Bayesian inference provide fast, flexible, deterministic alterna...
Many recent advances in large scale probabilistic inference rely on variational methods. The success...
Many recent advances in large scale probabilistic inference rely on variational methods. The success...
We propose a new class of learning algorithms that combines variational approximation and Markov cha...
We develop a method to combine Markov chain Monte Carlo (MCMC) and variational inference (VI), lever...
<div><p>Markov chain Monte Carlo approaches have been widely used for Bayesian inference. The drawba...
Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubi...
Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubi...
The central objective of this thesis is to develop new algorithms for inference in probabilistic gra...
The Markov Chain Monte Carlo (MCMC) technique provides a means to generate a random sequence of mode...
A key design constraint when implementing Monte Carlo and variational inference algorithms is that i...
Variational approximation methods are enjoying an increasing amount of development and use in statis...
Recent advances in stochastic gradient variational inference have made it possible to perform variat...
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 methods for approximate Bayesian inference provide fast, flexible, deterministic alterna...
Many recent advances in large scale probabilistic inference rely on variational methods. The success...
Many recent advances in large scale probabilistic inference rely on variational methods. The success...
We propose a new class of learning algorithms that combines variational approximation and Markov cha...
We develop a method to combine Markov chain Monte Carlo (MCMC) and variational inference (VI), lever...
<div><p>Markov chain Monte Carlo approaches have been widely used for Bayesian inference. The drawba...
Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubi...
Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubi...
The central objective of this thesis is to develop new algorithms for inference in probabilistic gra...
The Markov Chain Monte Carlo (MCMC) technique provides a means to generate a random sequence of mode...
A key design constraint when implementing Monte Carlo and variational inference algorithms is that i...
Variational approximation methods are enjoying an increasing amount of development and use in statis...