Scalable Metropolis-Hastings for exact Bayesian inference with large datasets
Bayesian inference via standard Markov Chain Monte Carlo (MCMC) methods such as Metropolis-Hastings is too computationally intensive to handle large datasets, since the cost per step usually scales like O(n) in the number of data points n. We propose the Scalable Metropolis-Hastings (SMH) kernel tha...
| Main Authors: | , , , , |
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| Format: | Conference item |
| Published: |
Journal of Machine Learning Research
2019
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