## An Approach to the Decomposition of Large Stochastic ModelsComputer Research Laboratory, University of California, Santa Cruz, 1993 - Decomposition (Mathematics) |

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accuracy analyzed in isolation approximate conditional throughput bimodal case shown computational effort involved computational savings Computer Research Laboratory conditional throughputs otter conditional throughputs u(n conditionol convergence curve fitting decomposition method divides the queueing equivalent model equivalent outer error in mean evaluate the subnetwork example used corresponds family of decomposition fixed point iteration full decomposition global dependencies global state dependencies inner model intermediate evaluation points intermediate points Introductory example lood maxima mean number mode point model into smaller number in system number of evaluation number of intermediate number of points operating regions outer model Ovotion partial evaluation points at iteration points evaluated points ot Iterotion population levels probability distribution p(m probobtlty queueing model queueing network queueing system relative error sampling Santa Cruz simple approach simple linear interpolation smaller subsystems stochastic models subnetwork in isolation subnetwork was solved subset system with global technique tends total number underlying our approach University of California