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Monte Carlo methods via a dual approach for some discrete time stochastic control problems
Gyurkó, Lajos Gergely; Hambly, Ben M.; Witte, Jan Hendrik - In: Mathematical methods of operations research 81 (2015) 1, pp. 109-135
Persistent link: https://www.econbiz.de/10010488925
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Convergence of Markov chain Monte Carlo algorithms
Polson, Nicholas G. - 1993
Persistent link: https://www.econbiz.de/10000899197
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MCMC methods for continuous-time financial econometrics
Johannes, Michael; Polson, Nicholas G. - 2010
Persistent link: https://www.econbiz.de/10003900732
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Augmented Markov chain Monte Carlo simulation for two-stage stochastic programs with recourse
Ekin, Tahir; Polson, Nicholas G.; Soyer, Refik - In: Decision analysis : a journal of the Institute for … 11 (2014) 4, pp. 250-264
Persistent link: https://www.econbiz.de/10010467454
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Sequential bayesian learning for stochastic volatility with variance-gamma jumps in returns
Warty, Samir P.; Lopes, Hehibert F.; Polson, Nicholas G. - 2014
Persistent link: https://www.econbiz.de/10010440180
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Markov Chain Monte Carlo
Johannes, Michael; Polson, Nicholas G. - In: Handbook of financial time series, (pp. 1001-1013). 2009
Persistent link: https://www.econbiz.de/10003834286
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Particle filtering
Johannes, Michael; Polson, Nicholas G. - In: Handbook of financial time series, (pp. 1015-1029). 2009
Persistent link: https://www.econbiz.de/10003834288
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MCMC maximum likelihood for latent state models
Jacquier, Eric; Johannes, Michael; Polson, Nicholas G. - In: Journal of econometrics 137 (2007) 2, pp. 615-640
Persistent link: https://www.econbiz.de/10003442024
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Particle learning for fat-tailed distributions
Lopes, Hedibert Freitas; Polson, Nicholas G. - In: Econometric reviews 35 (2016) 8/10, pp. 1666-1691
Persistent link: https://www.econbiz.de/10011592384