Searched for: author%3A%22Schauer%2C+M.R.%22
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Bierkens, G.N.J.C. (author), Grazzi, S. (author), van der Meulen, F.H. (author), Schauer, M.R. (author)
We construct a new class of efficient Monte Carlo methods based on continuous-time piecewise deterministic Markov processes (PDMPs) suitable for inference in high dimensional sparse models, i.e. models for which there is prior knowledge that many coordinates are likely to be exactly 0. This is achieved with the fairly simple idea of endowing...
journal article 2023
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Corstanje, M.A. (author), van der Meulen, F.H. (author), Schauer, M.R. (author)
A continuous-time Markov process X can be conditioned to be in a given state at a fixed time T>0 using Doob's h-transform. This transform requires the typically intractable transition density of X. The effect of the h-transform can be described as introducing a guiding force on the process. Replacing this force with an approximation...
journal article 2022
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Bierkens, G.N.J.C. (author), Grazzi, S. (author), van der Meulen, F.H. (author), Schauer, M.R. (author)
We introduce the use of the Zig-Zag sampler to the problem of sampling conditional diffusion processes (diffusion bridges). The Zig-Zag sampler is a rejection-free sampling scheme based on a non-reversible continuous piecewise deterministic Markov process. Similar to the Lévy–Ciesielski construction of a Brownian motion, we expand the...
journal article 2021
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Bierkens, G.N.J.C. (author), van der Meulen, F.H. (author), Schauer, M.R. (author)
Suppose X is a multidimensional diffusion process. Assume that at time zero the state of X is fully observed, but at time 0$ ]]> only linear combinations of its components are observed. That is, one only observes the vector for a given matrix L. In this paper we show how samples from the conditioned process can be generated. The main...
journal article 2020
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Schauer, M.R. (author), van der Meulen, F.H. (author), Van Zanten, Harry (author)
A Monte Carlo method for simulating a multi-dimensional diffusion process conditioned on hitting a fixed point at a fixed future time is developed. Proposals for such diffusion bridges are obtained by superimposing an additional guiding term to the drift of the process under consideration. The guiding term is derived via approximation of the...
journal article 2017
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van der Meulen, F.H. (author), Schauer, M.R. (author), van Waaij, Jan (author)
We consider the problem of nonparametric estimation of the drift of a continuously observed one-dimensional diffusion with periodic drift. Motivated by computational considerations, van der Meulen et al. (Comput Stat Data Anal 71:615–632, 2014) defined a prior on the drift as a randomly truncated and randomly scaled Faber–Schauder series...
journal article 2017
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van der Meulen, F.H. (author), Schauer, M.R. (author)
Estimation of parameters of a diffusion based on discrete time observations poses a difficult problem due to the lack of a closed form expression for the likelihood. From a Bayesian computational perspective it can be casted as a missing data problem where the diffusion bridges in between discrete-time observations are missing. The...
journal article 2017
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van der Meulen, F.H. (author), Schauer, M.R. (author)
We present a general framework for Bayesian estimation of incompletely observed multivariate diffusion processes. Observations are assumed to be discrete in time, noisy and incomplete. We assume the drift and diffusion coefficient depend on an unknown parameter. A data-augmentation algorithm for drawing from the posterior distribution is...
journal article 2017
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