Zobrazeno 1 - 10
of 187
pro vyhledávání: '"Meulen, Frank"'
Given a mild solution $X$ to a semilinear stochastic partial differential equation (SPDE), we consider an exponential change of measure based on its infinitesimal generator $L$, defined in the topology of bounded pointwise convergence. The changed me
Externí odkaz:
http://arxiv.org/abs/2409.08057
To date, most methods for simulating conditioned diffusions are limited to the Euclidean setting. The conditioned process can be constructed using a change of measure known as Doob's $h$-transform. The specific type of conditioning depends on a funct
Externí odkaz:
http://arxiv.org/abs/2403.05409
Autor:
Corstanje, Marc, van der Meulen, Frank
Let $X$ be a chemical reaction process, modeled as a multi-dimensional continuous-time jump process. Assume that at given times $0< t_1 < \cdots
Externí odkaz:
http://arxiv.org/abs/2312.04457
Publikováno v:
2021-2022 MATRIX Annals. MATRIX Book Series, vol 5. Springer, Cham, 527-568 (2024)
We present a survey of some of our recent results on Bayesian nonparametric inference for a multitude of stochastic processes. The common feature is that the prior distribution in the cases considered is on suitable sets of piecewise constant or piec
Externí odkaz:
http://arxiv.org/abs/2305.07432
Autor:
Schauer, Moritz, van der Meulen, Frank
Backward Filtering Forward Guiding (BFFG) is a bidirectional algorithm proposed in Mider et al. [2021] and studied more in depth in a general setting in Van der Meulen and Schauer [2022]. In category theory, optics have been proposed for modelling sy
Externí odkaz:
http://arxiv.org/abs/2303.13865
Autor:
van der Meulen, Frank
In this document I aim to give an informal treatment of automatic Backward Filtering Forward Guiding, a general algorithm for conditional sampling from a Markov process on a directed acyclic graph. I'll show that the underlying ideas can be understoo
Externí odkaz:
http://arxiv.org/abs/2203.04155
Publikováno v:
Stochastics 95(6), 2022, pp. 963-996
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 des
Externí odkaz:
http://arxiv.org/abs/2111.11377
Publikováno v:
Stat Comput 33, 8 (2023)
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 coo
Externí odkaz:
http://arxiv.org/abs/2103.08478
Autor:
van der Meulen, Frank, Schauer, Moritz
We incorporate discrete and continuous time Markov processes as building blocks into probabilistic graphical models with latent and observed variables. We introduce the automatic Backward Filtering Forward Guiding (BFFG) paradigm (Mider et al., 2021)
Externí odkaz:
http://arxiv.org/abs/2010.03509
Publikováno v:
SIAM Journal on Imaging Sciences 15 (1), 2022, pp. 293-323
Stochastically evolving geometric systems are studied in shape analysis and computational anatomy for modelling random evolutions of human organ shapes. The notion of geodesic paths between shapes is central to shape analysis and has a natural genera
Externí odkaz:
http://arxiv.org/abs/2002.00885