The ever-increasing size of data sets has resulted in an immense effort in Bayesian statistics to develop more expressive and scalable probabilistic models. Inference remains a challenge and limits the use of these models in large-scale scientific and industrial applications. Asymptotically exact schemes such as Markov chain Monte Carlo (MCMC) are often slow to run and difficult to evaluate in finite time. Thus we must resort to approximate inference, which allows for more efficient run times and more reliable convergence diagnostics on large-scale and streaming data—without compromising on the complexity of these models. This workshop aims to bring together researchers and practitioners in order to discuss recent advances in approximate inference; we also aim to discuss the methodological and foundational issues in such techniques in order to consider future improvements.

The resurgence of interest in approximate inference has furthered development in many techniques: for example, scalability, variance reduction, and preserving dependency in variational inference; divide and conquer techniques in expectation propagation; dimensionality reduction using random projections; and stochastic variants of Laplace approximation-based methods. Approximate inference techniques have clearly emerged as the preferred way to perform tractable Bayesian inference. Despite this interest, there remain significant trade-offs in speed, accuracy, generalizability, and learned model complexity. In this workshop, we will discuss how to rigorously characterize these tradeoffs, as well as how they might be made more favourable. Moreover, we will address the issues of its adoption in scientific communities which could benefit from advice on their practical usage and the development of relevant software packages.