2015/03/05 by Chopin, Nicolas, Gerber, Mathieu
#Computation (stat.CO) #FOS: Computer and information sciences
paper · doi:10.48550/arxiv.1503.01631
Sequential Monte Carlo algorithms (also known as particle filters) are popular methods to approximate filtering (and related) distributions of state-space models. However, they converge at the slow 1/√(N) rate, which may be an issue in real-time data-intensive scenarios. We give a brief outline of SQMC (Sequential Quasi-Monte Carlo), a variant of SMC based on low-discrepancy point sets proposed by Gerber and Chopin (2015), which converges at a faster rate, and we illustrate the greater performance of SQMC on autonomous positioning problems.