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Parallelizing MCMC with Random Partition Trees

Neural Information Processing Systems

The modern scale of data has brought new challenges to Bayesian inference. In particular, conventional MCMC algorithms are computationally very expensive for large data sets. A promising approach to solve this problem is embarrassingly parallel MCMC (EP-MCMC), which first partitions the data into multiple subsets and runs independent sampling algorithms on each subset. The subset posterior draws are then aggregated via some combining rules to obtain the final approximation. Existing EP-MCMC algorithms are limited by approximation accuracy and difficulty in resampling. In this article, we propose a new EP-MCMC algorithm PART that solves these problems. The new algorithm applies random partition trees to combine the subset posterior draws, which is distribution-free, easy to resample from and can adapt to multiple scales. We provide theoretical justification and extensive experiments illustrating empirical performance.


Merging MCMC Subposteriors through Gaussian-Process Approximations

arXiv.org Machine Learning

Markov chain Monte Carlo (MCMC) algorithms have become powerful tools for Bayesian inference. However, they do not scale well to large-data problems. Divide-and-conquer strategies, which split the data into batches and, for each batch, run independent MCMC algorithms targeting the corresponding subposterior, can spread the computational burden across a number of separate workers. The challenge with such strategies is in recombining the subposteriors to approximate the full posterior. By creating a Gaussian-process approximation for each log-subposterior density we create a tractable approximation for the full posterior. This approximation is exploited through three methodologies: firstly a Hamiltonian Monte Carlo algorithm targeting the expectation of the posterior density provides a sample from an approximation to the posterior; secondly, evaluating the true posterior at the sampled points leads to an importance sampler that, asymptotically, targets the true posterior expectations; finally, an alternative importance sampler uses the full Gaussian-process distribution of the approximation to the log-posterior density to re-weight any initial sample and provide both an estimate of the posterior expectation and a measure of the uncertainty in it.


Parallelising MCMC via Random Forests

arXiv.org Machine Learning

Markov chain Monte Carlo (MCMC) algorithm, a generic sampling method, is ubiquitous in modern statistics, especially in Bayesian fields. MCMC algorithms require only the evaluation of the target pointwise, up to a multiple constant, in order to sample from it. In Bayesian analysis, the object of main interest is the posterior, which is not in closed form in general, and MCMC has become a standard tool in this domain. However, MCMC is difficult to scale and its applications are limited when the observation size is very large, for it needs to sweep over the entire observations set in order to evaluate the likelihood function at each iteration. Recently, many methods have been proposed to better scale MCMC algorithms for big data sets and these can be roughly classified into two groups Bardenet et al. (2017): divide-and-conquer methods and subsampling-based methods. For divide-and-conquer methods, one splits the whole data set into subsets, runs MCMC over each subset to generate samples of parameters and combine these to produce an approximation of the true posterior. Depending on how MCMC is handled over the subsets, these methods can be further classified into two sub-categories.


Distributed Bayesian Posterior Sampling via Moment Sharing

Neural Information Processing Systems

We propose a distributed Markov chain Monte Carlo (MCMC) inference algorithm for large scale Bayesian posterior simulation. We assume that the dataset is partitioned and stored across nodes of a cluster. Our procedure involves an independent MCMC posterior sampler at each node based on its local partition of the data. Moment statistics of the local posteriors are collected from each sampler and propagated across the cluster using expectation propagation message passing with low communication costs. The moment sharing scheme improves posterior estimation quality by enforcing agreement among the samplers. We demonstrate the speed and inference quality of our method with empirical studies on Bayesian logistic regression and sparse linear regression with a spike-and-slab prior.


Parallelizing MCMC with Random Partition Trees

arXiv.org Machine Learning

The modern scale of data has brought new challenges to Bayesian inference. In particular, conventional MCMC algorithms are computationally very expensive for large data sets. A promising approach to solve this problem is embarrassingly parallel MCMC (EP-MCMC), which first partitions the data into multiple subsets and runs independent sampling algorithms on each subset. The subset posterior draws are then aggregated via some combining rules to obtain the final approximation. Existing EP-MCMC algorithms are limited by approximation accuracy and difficulty in resampling. In this article, we propose a new EP-MCMC algorithm PART that solves these problems. The new algorithm applies random partition trees to combine the subset posterior draws, which is distribution-free, easy to resample from and can adapt to multiple scales. We provide theoretical justification and extensive experiments illustrating empirical performance.