Approximate Bayesian Inference

Approximate Decentralized Bayesian Inference

This paper presents an approximate method for performing Bayesian inference in models with conditional independence over a decentralized network of learning agents. The method first employs variational inference on each individual learning agent to generate a local approximate posterior, the agents transmit their local posteriors to other agents in the network, and finally each agent combines i...

Approximate Bayesian Inference for Quantiles

Suppose data consist of a random sample from a distribution function FY , which is unknown, and that interest focuses on inferences on θ, a vector of quantiles of FY . When the likelihood function is not fully specified, a posterior density cannot be calculated and Bayesian inference is difficult. This article considers an approach which relies on a substitution likelihood characterized by a ve...

Approximate Inference in Structured Bayesian Networks

Approximate inference for the loss-calibrated Bayesian

In this framework talk, we consider the impact of approximate inference in the context of Bayesian decision theory. We argue for the need to shift focus from the traditional approach of solely approximating the Bayesian posterior to developing loss-calibrated approximate Bayesian inference methods, in analogy to what is done in discriminative machine learning. We outline research questions whic...

Approximate Slice Sampling for Bayesian Posterior Inference

In this paper, we advance the theory of large scale Bayesian posterior inference by introducing a new approximate slice sampler that uses only small mini-batches of data in every iteration. While this introduces a bias in the stationary distribution, the computational savings allow us to draw more samples in a given amount of time and reduce sampling variance. We empirically verify on three dif...