Data Driven Markov Chain Monte Carlo

Adrian Barbu and Song-Chun Zhu
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Adrian Barbu: Florida State University, Department of Statistics
Song-Chun Zhu: University of California, Los Angeles, Departments of Statistics and Computer Science

Chapter 8 in Monte Carlo Methods, 2020, pp 211-280 from Springer

Abstract: Abstract Data Driven Markov chain Monte Carlo (DDMCMC) provides a principled way to use low-level information from processes such as edge detection and clustering to guide the MCMC search by making informed jumps in the solution space, achieving significant speed-ups in convergence to the modes of the posterior probability. The data-driven information obtained from edge detection and intensity clustering in represented as weighted samples (particles) and used as importance proposal probabilities for MCMC jumps using the Metropolis-Hastings method.

Date: 2020
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DOI: 10.1007/978-981-13-2971-5_8

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