A Bayesian framework for quantifying uncertainty in stochastic simulation
When we use simulation to estimate the performance of a stochastic system, the simulation
often contains input models that were estimated from real-world data; therefore, there is both …
often contains input models that were estimated from real-world data; therefore, there is both …
[引用][C] A Bayesian Framework for Quantifying Uncertainty in Stochastic Simulation.
W Xie, BL Nelson, RR Barton - Operations research, 2014 - dialnet.unirioja.es
A Bayesian Framework for Quantifying Uncertainty in Stochastic Simulation
W Xie, BL Nelson, RR Barton - Operations Research, 2014 - dl.acm.org
When we use simulation to estimate the performance of a stochastic system, the simulation
often contains input models that were estimated from real-world data; therefore, there is both …
often contains input models that were estimated from real-world data; therefore, there is both …
A bayesian framework for quantifying uncertainty in stochastic simulation
W Xie, BL Nelson, RR Barton - Operations Research, 2014 - pure.psu.edu
When we use simulation to estimate the performance of a stochastic system, the simulation
often contains input models that were estimated from real-world data; therefore, there is both …
often contains input models that were estimated from real-world data; therefore, there is both …
A Bayesian Framework for Quantifying Uncertainty in Stochastic Simulation
W Xie, BL Nelson, RR Barton - Operations Research, 2014 - JSTOR
When we use simulation to estimate the performance of a stochastic system, the simulation
often contains input models that were estimated from real-world data; therefore, there is both …
often contains input models that were estimated from real-world data; therefore, there is both …
A Bayesian Framework for Quantifying Uncertainty in Stochastic Simulation
W Xie, BL Nelson, RR Barton - Operations Research, 2014 - ideas.repec.org
When we use simulation to estimate the performance of a stochastic system, the simulation
often contains input models that were estimated from real-world data; therefore, there is both …
often contains input models that were estimated from real-world data; therefore, there is both …
A Bayesian Framework for Quantifying Uncertainty in Stochastic Simulation
W Xie, BL Nelson, RR Barton - Operations Research, 2014 - econpapers.repec.org
When we use simulation to estimate the performance of a stochastic system, the simulation
often contains input models that were estimated from real-world data; therefore, there is both …
often contains input models that were estimated from real-world data; therefore, there is both …
A Bayesian Framework for Quantifying Uncertainty in Stochastic Simulation.
W Xie, BL Nelson, RR Barton - Operations Research, 2014 - search.ebscohost.com
When we use simulation to estimate the performance of a stochastic system, the simulation
often contains input models that were estimated from real-world data; therefore, there is both …
often contains input models that were estimated from real-world data; therefore, there is both …
A bayesian framework for quantifying uncertainty in stochastic simulation
W Xie, BL Nelson, RR Barton - Operations Research, 2014 - scholars.northwestern.edu
When we use simulation to estimate the performance of a stochastic system, the simulation
often contains input models that were estimated from real-world data; therefore, there is both …
often contains input models that were estimated from real-world data; therefore, there is both …