12 citations · 27 across the 9 of their papers we have counts for
9 papers
Neural Backward Filtering Forward Guiding
Gefan Yang, Frank van der Meulen, Stefan Sommer
Inference in nonlinear continuous stochastic processes on trees is challenging, particularly when observations are sparse and the topology is complex. Exact smoothing via Doob's $h…
Simulating conditioned diffusions on manifolds
Marc Corstanje, Frank van der Meulen, Moritz Schauer +1
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…
Guided simulation of conditioned chemical reaction networks
Marc Corstanje, Frank van der Meulen
Let be a chemical reaction process, modeled as a multi-dimensional continuous-time jump process. Assume that at given times , linear combinations $v_i = L…
Nonparametric Bayesian inference for stochastic processes with piecewise constant priors
Denis Belomestny, Frank van der Meulen, Peter Spreij
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…
Introduction to Automatic Backward Filtering Forward Guiding
Frank van der Meulen
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 direct…
Bayesian estimation of a decreasing density
Geurt Jongbloed, Frank van der Meulen, Lixue Pang
Suppose is a random sample from a bounded and decreasing density on . We are interested in estimating such , with special interest in $f_0(0…
Nonparametric Bayesian estimation of a Hölder continuous diffusion coefficient
Shota Gugushvili, Frank van der Meulen, Moritz Schauer +1
We consider a nonparametric Bayesian approach to estimate the diffusion coefficient of a stochastic differential equation given discrete time observations over a fixed time interva…
A non-parametric Bayesian approach to decompounding from high frequency data
Shota Gugushvili, Frank van der Meulen, Peter Spreij
Given a sample from a discretely observed compound Poisson process, we consider non-parametric estimation of the density of its jump sizes, as well as of its intensity …
Nonparametric Bayesian inference for multidimensional compound Poisson processes
Shota Gugushvili, Frank van der Meulen, Peter Spreij
Given a sample from a discretely observed multidimensional compound Poisson process, we study the problem of nonparametric estimation of its jump size density and intensity $…