Exact simulation of the first-passage time of diffusionsFirst-passage timeBrownian motionDiffusion processesGirsanov’s transformationExact simulationRandomized algorithmSince diffusion processes arise in so many different fields, efficient technics for the simulation of sample paths, like discretization schemes,...
(2008) A new factorisation of diffusion measure and finite sample path construction. In: Methodology and Computing in Applied Probability, vol 10, No. 1, pp 85–104. Beskos A., Roberts G.O. (2005) Exact simulation of diffusions In: Ann. Appl. Probab, vol 15, pp 2422–2444. ...
A version of this method allows to obtain realizations also from the law of the process conditioned on taking given values at the extremal points of a time interval (diffusion bridge). This is of particular interest because simulation of conditioned diffusions is often needed in some inferential ...
To reveal the hidden neuronal motifs, we need a framework for judging how the hidden network of input neurons shapes the complex joint activity of postsynaptic neurons, possibly characterized by their higher-order interactions37,38. This framework, in turn, could be used as a tool to infer the...
This work deals with the simulation of Wishart processes and affine diffusions on positive semidefinite matrices. To do so, we focus on the splitting of the infinitesimal generator in order to use composition techniques as did Ninomiya and Victoir [Appl. Math. Finance 15 (2008) 107-121] or Al...
exact simulation methodsskew Brownian motionskew diffusionsBrownian motion with discontinuous driftIn this paper, using an algorithm based on the retrospective rejection sampling scheme introduced in [A. Beskos, O. Papaspiliopoulos, and G. O. Roberts,Methodol. Comput. Appl. Probab., 10 (2008), ...
This algorithm is based on both the exact simulation of the diffusion value at a fixed time and on the exact simulation of the first passage time for continuous diffusion processes (see Herrmann and Zucca (2019)). At a fixed point in time, the main challenge is to generate the position ...
exact simulationMarkov chain Monte Carlostochastic differential equationtransition densityWe develop exact Markov chain Monte Carlo methods for discretely sampled, directly and indirectly observed diffusions. The qualification 'exact' refers to the fact that the invariant and limiting distribution of the ...
The objective of the paper is to present a novel methodology for likelihood-based inference for discretely observed diffusions. We propose Monte Carlo methods, which build on recent advances on the exact simulation of diffusions, for performing maximum likelihood and Bayesian estimation....
Our sampler is applicable to the problem of prior simulation from an SDE, posterior simulation conditioned on noisy observations, as well as parameter inference given noisy observations. Our work recasts an existing rejection sampling algorithm for a class of diffusions as a latent variable model, ...