The rapid growth in all forms of electronic publishing is creating many new problems – both technical and socio-economic. This paper examines some of these from three different ...
Importance sampling-based algorithms are a popular alternative when Bayesian network models are too large or too complex for exact algorithms. However, importance sampling is sensi...
This paper develops importance resampling into a variance reduction technique for Monte Carlo integration. Importance resampling is a sample generation technique that can be used ...
Image-based representations for illumination can capture complex real-world lighting that is difficult to represent in other forms. Current importance sampling strategies for ima...
Typically agent evaluation is done through Monte Carlo estimation. However, stochastic agent decisions and stochastic outcomes can make this approach inefficient, requiring many s...
Michael H. Bowling, Michael Johanson, Neil Burch, ...