Sequential Monte Carlo

Adrian Barbu and Song-Chun Zhu
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Adrian Barbu: Florida State University, Department of Statistics
Song-Chun Zhu: University of California, Los Angeles, Departments of Statistics and Computer Science

Chapter 2 in Monte Carlo Methods, 2020, pp 19-48 from Springer

Abstract: Abstract Sequential Monte Carlo Sequential Monte Carlo (SMC) is used when the distribution of interest is one-dimensional or multi-dimensional and factorizable. If f(x) denotes the true probability distribution function controlling a process and π(x) denotes a target probability distribution based on a model, then the goal is to find a model to make the target density function π(x) converge to f(x). In order to find this model, a known, trial probability density g(x) may be used. In this chapter several concepts related to the selection of g(x) for SMC are covered including sample weighting and importance sampling. Applications covered include self-avoiding walks, Parzen windows, ray tracing, particle filtering, and glossy highlights. The chapter ends with a discussion of Monte Carlo Tree Search.

Date: 2020
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DOI: 10.1007/978-981-13-2971-5_2

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