Examples comparing Importance Sampling and the Metropolis algorithm

@inproceedings{Bassetti2005ExamplesCI,
  title={Examples comparing Importance Sampling and the Metropolis algorithm},
  author={Federico Bassetti and Persi Diaconis},
  year={2005}
}
Importance sampling, particularly sequential and adaptive importance sampling, have emerged as competitive simulation techniques to Markov–chain Monte–Carlo techniques. We compare importance sampling and the Metropolis algorithm as two ways of changing the output of a Markov chain to get a different stationary distribution. 
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