Stochastic Gradient Monomial Gamma Sampler
نویسندگان
چکیده
Recent advances in stochastic gradient techniques have made it possible to estimate posterior distributions from large datasets via Markov Chain Monte Carlo (MCMC). However, when the target posterior is multimodal, mixing performance is often poor. This results in inadequate exploration of the posterior distribution. A framework is proposed to improve the sampling efficiency of stochastic gradient MCMC, based on Hamiltonian Monte Carlo. A generalized kinetic function is leveraged, delivering superior stationary mixing, especially for multimodal distributions. Techniques are also discussed to overcome the practical issues introduced by this generalization. It is shown that the proposed approach is better at exploring complex multimodal posterior distributions, as demonstrated on multiple applications and in comparison with other stochastic gradient MCMC methods.
منابع مشابه
A Complete Recipe for Stochastic Gradient MCMC
Many recent Markov chain Monte Carlo (MCMC) samplers leverage continuous dynamics to define a transition kernel that efficiently explores a target distribution. In tandem, a focus has been on devising scalable variants that subsample the data and use stochastic gradients in place of full-data gradients in the dynamic simulations. However, such stochastic gradient MCMC samplers have lagged behin...
متن کاملLearning Deep Generative Models with Doubly Stochastic MCMC
We present doubly stochastic gradient MCMC, a simple and generic method for (approximate) Bayesian inference of deep generative models in the collapsed continuous parameter space. At each MCMC sampling step, the algorithm randomly draws a minibatch of data samples to estimate the gradient of log-posterior and further estimates the intractable expectation over latent variables via a Gibbs sample...
متن کاملActive Sampler: Light-weight Accelerator for Complex Data Analytics at Scale
Recent years have witnessed amazing outcomes from “Big Models” trained by “Big Data”. Most popular algorithms for model training are iterative. Due to the surging volumes of data, we can usually afford to process only a fraction of the training data in each iteration. Typically, the data are either uniformly sampled or sequentially accessed. In this paper, we study how the data access pattern c...
متن کاملLearning to Sample Using Stein Discrepancy
We propose a simple algorithm to train stochastic neural networks to draw samples from given target distributions for probabilistic inference. Our method is based on iteratively adjusting the neural network parameters so that the output changes along a Stein variational gradient [1] that maximumly decreases the KL divergence with the target distribution. Our method works for any target distribu...
متن کاملStochastic Bouncy Particle Sampler
We introduce a stochastic version of the nonreversible, rejection-free Bouncy Particle Sampler (BPS), a Markov process whose sample trajectories are piecewise linear, to efficiently sample Bayesian posteriors in big datasets. We prove that in the BPS no bias is introduced by noisy evaluations of the log-likelihood gradient. On the other hand, we argue that efficiency considerations favor a smal...
متن کامل