An Introduction to Sequential Monte Carlo
By Nicolas Chopin, Omiros Papaspiliopoulos. Springer Series in Statistics, 2020
505 citations recorded by the source corpus.
Subfields: Management Science and Operations Research, Numerical Analysis, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/a678db71b803ada578344bcb09e8f9314eff4dec
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Management Science and Operations Research
- Florian Huber
- John M. Maheu
- Ruben Loaiza-Maya
- Gael M. Martin
- Anastasios Panagiotelis
- Didier Nibbering
- David T. Frazier
- Worapree Maneesoonthorn
In Statistics and Probability
- Ruben Loaiza-Maya
- Gary Koop
- Gael M. Martin
- John M. Maheu
- Anastasios Panagiotelis
- Didier Nibbering
- David T. Frazier
- Florian Huber
Citations in the corpus
- Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem (2022, unknown): “[6] Nicolas Chopin and Omiros Papaspiliopoulos.”
- Bayesian forecasting in economics and finance: A modern review (2022, methodology): “…outlined below in Section 3.1; with more recent developments of both IS and MCMC – most notably sequential Monte Carlo (SMC) (Gordon et al., 1993; Chopin and Papaspiliopoulos, 2020) and pseudo-marginal MCMC (Beaumont, 2003; Andrieu and Roberts, 2009; Andrieu et al., 2011) – discussed briefly…”
- Practical and Asymptotically Exact Conditional Sampling in Diffusion Models (2023, unknown): “The defining property of SMC is that the weighted particles at each t form discrete approximations ) (where δ is a Dirac measure) to ν t ( x t ) that become arbitrarily accurate in the limit that many particles are used (Chopin and Papaspiliopoulos, 2020, Proposition 11.4).”