Summary
So far, the algorithms we have discussed rely on Monte Carlo, that is, on averages of random variables. QMC (quasi-Monte Carlo) is an alternative to Monte Carlo where random points are replaced with low-discrepancy sequences. The advantage is that QMC estimates usually converge faster than their Monte Carlo counterparts.
This chapter explains how to derive QMC particle algorithms, also called SQMC (sequential quasi-Monte Carlo) algorithms.
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Chopin, N., Papaspiliopoulos, O. (2020). Sequential Quasi-Monte Carlo. In: An Introduction to Sequential Monte Carlo. Springer Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-47845-2_13
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DOI: https://doi.org/10.1007/978-3-030-47845-2_13
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