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.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
