Using Random Quasi-Monte-Carlo Within Particle Filters, With Application to Financial Time Series

By Paul Fearnhead. 2005

44 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Finance, Probabilistic & Bayesian Methods

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Simon Lacoste-Julien
2. Francis R. Bach
3. Fredrik Lindsten
4. Peter Richtárik
5. Mathieu Gerber
6. Martin Jaggi
7. Michael I. Jordan
8. Nicolas Loizou

In Finance

1. Nicolas Chopin
2. Simon Lacoste-Julien
3. Francis R. Bach
4. Fredrik Lindsten
5. Christiane Lemieux
6. Mathieu Gerber
7. Csaba Szepesvari
8. Zhonghua Han

In Probabilistic & Bayesian Methods

1. Simon Lacoste-Julien
2. Francis R. Bach
3. Fredrik Lindsten
4. Christiane Lemieux
5. Mathieu Gerber
6. Nicolas Chopin
7. Michael W. Mahoney
8. Ivan Laptev

Citations in the corpus

• Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering (2015, methodology): “Then we have: where ˆ k W k and ˆ (cid:15) t is the FW error reported at time t by the algorithm: We note that χ t ≈ 1 as we expect the errors on W k to go in either direction, and thus to cancel each other over time (though in the worst case it could grow exponentially in t ).”
• Monte Carlo and Quasi-Monte Carlo Sampling (2009, background): “In addition to the reference [354] mentioned above, other papers that discuss the use of quasi–Monte Carlo within bootstrap filters are [117, 376].”
• Sequential quasi Monte Carlo (2015, background): “(2001) and Fearnhead (2005). The first paper casts SMC as a Monte Carlo algorithm in Td dimensions, where d = dimX , and therefore generates low-discrepancy sequences in [0, 1)Td.”