We survey possible strategies to improve the performance of Markov chain Monte Carlo methods either by reducing the asymptotic variance of the resulting estimators or by increasing the speed of convergence to stationarity. Recent advances in the direction of the pseudomarginal approach, Gradient-based algorithms and Approximate Bayesian Computation are also highlighted.

Peluso, S., Mira, A., Voce "Convergence and Mixing in Markov Chain Monte Carlo: Advanced Algorithms and Latest Developments", in Encyclopedia of Statistics in Quality and Reliability, John Wiley and Sons, Inc, In press 2015: N/A-N/A. 10.1002/9780470061572 [http://hdl.handle.net/10807/66037]

Convergence and Mixing in Markov Chain Monte Carlo: Advanced Algorithms and Latest Developments

Peluso, Stefano;
2015

Abstract

We survey possible strategies to improve the performance of Markov chain Monte Carlo methods either by reducing the asymptotic variance of the resulting estimators or by increasing the speed of convergence to stationarity. Recent advances in the direction of the pseudomarginal approach, Gradient-based algorithms and Approximate Bayesian Computation are also highlighted.
Inglese
Encyclopedia of Statistics in Quality and Reliability
9780470018613
Peluso, S., Mira, A., Voce "Convergence and Mixing in Markov Chain Monte Carlo: Advanced Algorithms and Latest Developments", in Encyclopedia of Statistics in Quality and Reliability, John Wiley and Sons, Inc, In press 2015: N/A-N/A. 10.1002/9780470061572 [http://hdl.handle.net/10807/66037]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/66037
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