Nested Sequential Monte Carlo Methods
By C. A. Naesseth, Fredrik Lindsten, Thomas Bo Schön. 2015
88 citations recorded by the source corpus.
We propose nested sequential Monte Carlo (NSMC), a methodology to sample from sequences of probability distributions, even where the random variables are high-dimensional. NSMC generalises the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correct SMC algorithm. Furthermore, NSMC can in itself be used to produce such properly weighted samples. Consequently, one NSMC sampler can be used to construct an efficient high-dimensional proposal distribution for another NSMC sampler, and this nesting of the algorithm can be done to an arbitrary degree. This allows us to consider complex and high-dimensional models using SMC. We show results that motivate the efficacy of our approach on several filtering problems with dimensions in the order of 100 to 1 000.
Subfields: Artificial Intelligence, 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
- Frank D. Wood
- Hongseok Yang
- Brooks Paige
- Jan-Willem van de Meent
- Tom Rainforth
- David M. Blei
- Rajesh Ranganath
- Yura N. Perov
In Probabilistic & Bayesian Methods
- Frank D. Wood
- Hongseok Yang
- Brooks Paige
- Jan-Willem van de Meent
- Tom Rainforth
- David M. Blei
- Rajesh Ranganath
- Scott W. Linderman
Citations in the corpus
- An Introduction to Probabilistic Programming (2018, background): “(2018) warnings about convergence rates for nested sampling and also utilize sampling methodologies that are specifically tailored to this situation (Rainforth, 2018; Naesseth et al., 2015).”
- Nesting Probabilistic Programs (2018, background): “We can thus view inference on such problems as taking a pseudo-marginal approach [1, 2], provided that all samples returned from the inner query are properly weighted [19].”
- Variational Sequential Monte Carlo (2017, methodology): “Naesseth et al. [2015] uses a Monte Carlo approximation to the (locally) optimal proposal [Doucet and Johansen, 2009].”
- Interacting Particle Markov Chain Monte Carlo (2016, background): “…and the extension by Lindsten et al. (2014) have found numerous applications in e.g. Bayesian non-parametrics (Valera et al., 2015; Tripuraneni et al., 2015), probabilistic programming (Wood et al., 2014; van de Meent et al., 2015) and graphical models (Everitt, 2012; Naesseth et al., 2014, 2015).”