Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets

28 Jan 2019Robert CornishPaul VanettiAlexandre Bouchard-CôtéGeorge DeligiannidisArnaud Doucet

Bayesian inference via standard Markov Chain Monte Carlo (MCMC) methods is too computationally intensive to handle large datasets, since the cost per step usually scales like $\Theta(n)$ in the number of data points $n$. We propose the Scalable Metropolis-Hastings (SMH) kernel that exploits Gaussian concentration of the posterior to require processing on average only $O(1)$ or even $O(1/\sqrt{n})$ data points per step... (read more)

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