SMC Samplers for Bayesian Optimal Nonlinear Design

Hendrik Kuck, Nando de Freitas, Arnaud Doucet
2006 2006 IEEE Nonlinear Statistical Signal Processing Workshop  
Experimental design is a fundamental problem in science. It arises in the planning of medical trials, sensor network deployment and control as well as in costly data gathering in physics, chemistry and biology. Bayesian decision theory provides a principled way of treating this problem, but leads to an intractable joint optimization and integration problem. Here, we propose a viable solution to this hard computational problem using sequential Monte Carlo samplers.
doi:10.1109/nsspw.2006.4378829 fatcat:gyndksb7crdx7jainrvnsx6xem