Publication Date:
2019-12-03
Description:
Existing algorithms like nested sampling and annealed importance sampling are able to produce accurate estimates of the marginal likelihood of a model, but tend to scale poorly to large data sets. This is because these algorithms need to recalculate the log-likelihood for each iteration by summing over the whole data set. Efficient scaling to large data sets requires that algorithms only visit small subsets (mini-batches) of data on each iteration. To this end, we estimate the marginal likelihood via a sequential decomposition into a product of predictive distributions p ( y n | y 〈 n ) . Predictive distributions can be approximated efficiently through Bayesian updating using stochastic gradient Hamiltonian Monte Carlo, which approximates likelihood gradients using mini-batches. Since each data point typically contains little information compared to the whole data set, the convergence to each successive posterior only requires a short burn-in phase. This approach can be viewed as a special case of sequential Monte Carlo (SMC) with a single particle, but differs from typical SMC methods in that it uses stochastic gradients. We illustrate how this approach scales favourably to large data sets with some simple models.
Electronic ISSN:
2504-3900
Topics:
Technology
