Population-based variance reduction for dynamic Monte Carlo

Variance Reduction Techniques in Monte Carlo Methods

Monte Carlo methods are simulation algorithms to estimate a numerical quantity in a statistical model of a real system. These algorithms are executed by computer programs. Variance reduction techniques (VRT) are needed, even though computer speed has been increasing dramatically, ever since the introduction of computers. This increased computer power has stimulated simulation analysts to develo...

Variance Reduction in Monte-Carlo Tree Search

Monte-Carlo Tree Search (MCTS) has proven to be a powerful, generic planning technique for decision-making in single-agent and adversarial environments. The stochastic nature of the Monte-Carlo simulations introduces errors in the value estimates, both in terms of bias and variance. Whilst reducing bias (typically through the addition of domain knowledge) has been studied in the MCTS literature...

Monte Carlo Integration and Variance Reduction

Variance reduction for Monte Carlo solutions of the Boltzmann equation

We show that by considering only the deviation from equilibrium, significant computational savings can be obtained in Monte Carlo evaluations of the Boltzmann collision integral for flow problems in the small Mach number sMad limit. The benefits of this variance reduction approach include a significantly reduced statistical uncertainty when the deviation from equilibrium is small, and a flow-ve...

Variance Reduction for Monte Carlo Implementation of Adaptive Sensor Management

Adaptive sensor management (scheduling) is usually formulated as a finite horizon POMDP and implemented using sequential Monte Carlo. In Monte Carlo, variance reduction is important for the reliable performance of the sensor scheduler. In this paper, we propose a Control Variate method for variance reduction when the sensor is scheduled using the Kullbach Leibler criterion.