G.N.J.C. Bierkens
48 records found
1
Authored
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 sa ...
Recently, there have been conceptually new developments in Monte Carlo methods through the introduction of new MCMC and sequential Monte Carlo (SMC) algorithms which are based on continuous-time, rather than discrete-time, Markov processes. This has led to some fundamentally n ...
Piecewise Deterministic Monte Carlo algorithms enable simulation from a posterior distribution, whilst only needing to access a sub-sample of data at each iteration. We show how they can be implemented in settings where the parameters live on a restricted domain.
@enIn Turitsyn, Chertkov and Vucelja [Phys. D 240 (2011) 410-414] a nonreversible Markov Chain Monte Carlo (MCMC) method on an augmented state space was introduced, here referred to as Lifted Metropolis-Hastings (LMH). A scaling limit of the magnetization process in the Curie-Wei ...
Contributed
Jobfeed alarm system
Applying change point detection and a particle filter to a random walk
Efficient Inference with Panel Data
On the pass-through of the Dutch 2001 and 2012 VAT increases to consumer prices
likelihood estimation was used to estimate parameters in the Ising model
and the exponential random graph model. The method and the models
where described mathematically and problems that occurred during th ...