This aim of this 12-month SAMSI program was to develop new approaches to scientific statistical computing using innovative sequential Monte Carlo (SMC) methods. The program addressed fundamental challenges in developing effective sequential and adaptive simulation methods for computations underlying inference and decision analysis. The research blended conceptual innovation in new and emerging methods with evaluation in substantial applied contexts drawn from areas such as control, communications and robotics engineering, financial and macro-economics, among others. Researchers from statistics, computer science, information engineering and applied mathematics were involved, and the program promoted the opportunity for both methodological and theoretical research. The interdisciplinary aspects of the program were substantial, as was the attractiveness for students and postdocs.
Working Groups:
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- Tracking and Large-Scale Dynamical Systems
- Theory
- Population Monte Carlo
- Particle Learning
- Model Assessment and Adaptive Design
- Continuous Time
- Big Data and Distributed Computing
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** To see more in depth information on this program, see the report HERE **