2 citations · 2 across the 3 of their papers we have counts for
Showing 2018Show all
3 papers · 1 filter
stat.CO2018
Perturbation Bounds for Monte Carlo within Metropolis via Restricted Approximations
Felipe Medina-Aguayo, Daniel Rudolf, Nikolaus Schweizer
The Monte Carlo within Metropolis (MCwM) algorithm, interpreted as a perturbed Metropolis-Hastings (MH) algorithm, provides an approach for approximate sampling when the target dis…
stat.CO2018
On a Metropolis-Hastings importance sampling estimator
Daniel Rudolf, Björn Sprungk
A classical approach for approximating expectations of functions w.r.t. partially known distributions is to compute the average of function values along a trajectory of a Metropoli…
math.ST2018
Maximum likelihood estimation in hidden Markov models with inhomogeneous noise
Manuel Diehn, Axel Munk, Daniel Rudolf
We consider parameter estimation in finite hidden state space Markov models with time-dependent inhomogeneous noise, where the inhomogeneity vanishes sufficiently fast. Based on th…