Stochastic variational inference is an efficient Bayesian inference technology for massive
datasets, which approximates posteriors by using noisy gradient estimates. Traditional …

In this article, two novel distributed variational Bayesian (VB) algorithms for a general class
of conjugate-exponential models are proposed over synchronous and asynchronous sensor …

Distributed inference/estimation in Bayesian framework in the context of sensor networks
has recently received much attention due to its broad applicability. The variational Bayesian …

In this paper we present an approach for scaling up Bayesian learning using variational
methods by exploiting distributed computing clusters managed by modern big data …

Motivated by a real-world financial dataset, we propose a distributed variational message
passing scheme for learning conjugate exponential models. We show that the method can …

Latent space models are popular for analyzing dynamic network data. We propose a
variational approach to estimate the model parameters and the latent positions of the nodes …

How to transform a family of simple distributions to approximate an intractable posterior
distribution in a scalable manner is a key problem in variational inference. Recent …

Inferring multilayer diffusion networks from observed cascades is both crucial and realistic.
To infer multilayer diffusion networks, constructing continuous-time diffusion models that …

This paper makes two contributions to Bayesian machine learning algorithms. Firstly, we
propose stochastic natural gradient expectation propagation (SNEP), a novel alternative to …

Trust is known as an important social concept and an effective factor in all human
interactions in social networks. Users tend to interact with trusted people with whom they …