Retrospective exact simulation of diffusion sample paths with applications

By Alexandros Beskos, Omiros Papaspiliopoulos, Gareth O. Roberts. 2006

258 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Condensed Matter Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/704e73c591b70b03c468f981af0395e4293e13db

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In Artificial Intelligence

In Condensed Matter Physics

In Statistics and Probability

Citations in the corpus

On nonnegative unbiased estimators (2013, background): “As described in Beskos et al.(2006), unbiased estimates of the integral ∫ T t=0 Φ(xt) dt can be obtained by standard importance sampling.”
Penalized nonparametric mean square estimation of the coefficients of diffusion processes (2007, methodology): “Using (9), (10) and (12), we have γn(t) − γn(b) = ‖t − b‖2n + 2 n n∑ k=1 (b − t)(Xk )Zk + 2 n n∑ k=1 (b − t)(Xk ) ∫ (k+1) k ( b(Xs) − b(Xk ) ) ds.”
Particle filters for partially observed diffusions (2007, methodology): “This is based on an extension of the Poisson estimator (PE) of Beskos et al. (2006b), which we call the generalized Poisson estimator (GPE).”
A Factorisation of Diffusion Measure and Finite Sample Path Constructions (2008, methodology): “Beskos et al. (2006) replaced condition (**) with the weaker (*).”
Efficient MCMC for Climate Model Parameter Estimation: Parallel Adaptive Chains and Early Rejection (2012, methodology): “See (Beskos et al. 2006b; Dunson and Park 2008) for further applications.”
Likelihood-based inference for Matérn type-III repulsive point processes (2009, methodology): “This idea of using an auxiliary Poisson process to generate a Bernoulli distributed random variate without the need to calculate the parameter exactly also appears in [2], where it was used in the very different context of generating diffusion sample paths.”
On Simulated Likelihood of Discretely Observed Diffusion Processes and Comparison to Closed-Form Approximation (2007, unknown): “The exact simulation approach is also a data augmentation method but is built on exact simulation of diffusion sample paths (Beskos and Roberts 2005; Beskos et al. 2006a).”
On the exact and ε-strong simulation of (jump) diffusions (2013, methodology): “In developing a methodological framework for simulating exact skeletons of (jump) diffusion sample paths we are able to present a number of significant extensions to the existing literature on exact algorithms [5,6,13,20].”
Exact and computationally efficient likelihood‐based estimation for discretely observed diffusion processes (with discussion) (2006, methodology): “This is done in Beskos et al. (2004b), where also other important issues related to the implementation of the MCMC algorithm are tackled.”