Stationarity and Convergence of the Metropolis-Hastings Algorithm: Insights into Theoretical Aspects

@article{Hill2019StationarityAC,
  title={Stationarity and Convergence of the Metropolis-Hastings Algorithm: Insights into Theoretical Aspects},
  author={Stacy D. Hill and James C. Spall},
  journal={IEEE Control Systems},
  year={2019},
  volume={39},
  pages={56-67}
}
Markov chain Monte Carlo (MCMC) is a versatile sampling approach that is useful in a wide range of estimation and simulation applications. Fundamentally, MCMC is a powerful general means of generating random samples from probability distributions from which it is otherwise difficult to draw samples. The MCMC method is named for its reliance on the construction of a Markovian (dependent) sequence of random variables. Under modest conditions, the sequence has a limiting probability distribution… Expand
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