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An Introduction to Sequential Monte Carlo Methods

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Part of the Statistics for Engineering and Information Science book series (ISS)

Abstract

Many real-world data analysis tasks involve estimating unknown quantities from some given observations. In most of these applications, prior knowledge about the phenomenon being modelled is available. This knowledge allows us to formulate Bayesian models, that is prior distributions for the unknown quantities and likelihood functions relating these quantities to the observations. Within this setting, all inference on the unknown quantities is based on the posterior distribution obtained from Bayes’ theorem. Often, the observations arrive sequentially in time and one is interested in performing inference on-line. It is therefore necessary to update the posterior distribution as data become available. Examples include tracking an aircraft using radar measurements, estimating a digital communications signal using noisy measurements, or estimating the volatility of financial instruments using stock market data. Computational simplicity in the form of not having to store all the data might also be an additional motivating factor for sequential methods.

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  • DOI: 10.1007/978-1-4757-3437-9_1
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© 2001 Springer Science+Business Media New York

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Doucet, A., de Freitas, N., Gordon, N. (2001). An Introduction to Sequential Monte Carlo Methods. In: Doucet, A., de Freitas, N., Gordon, N. (eds) Sequential Monte Carlo Methods in Practice. Statistics for Engineering and Information Science. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-3437-9_1

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  • DOI: https://doi.org/10.1007/978-1-4757-3437-9_1

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4419-2887-0

  • Online ISBN: 978-1-4757-3437-9

  • eBook Packages: Springer Book Archive