• DOI:10.1109/MCS.2018.2876959
Corpus ID: 58672766

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={S. Hill and J. Spall},
  journal={IEEE Control Systems},
  year={2019},
  volume={39},
  pages={56-67}
}

S. Hill, J. Spall

Published 2019

Engineering

IEEE Control Systems

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… CONTINUE READING

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