21 citations · 34 across the 6 of their papers we have counts for
6 papers
Non-asymptotic Estimation of Sparse Partial Correlation via Joint Partial Regression
Samuel Erickson, Tobias Rydén
Partial correlation analysis is widely used in modern multivariate analysis. However, prior methods lack non-asymptotic guarantees and have slow asymptotic estimation rates. We pre…
Metropolising forward particle filtering backward sampling and Rao-Blackwellisation of Metropolised particle smoothers
Jimmy Olsson, Tobias Rydén
Smoothing in state-space models amounts to computing the conditional distribution of the latent state trajectory, given observations, or expectations of functionals of the state tr…
Subspace estimation and prediction methods for hidden Markov models
Sofia Andersson, Tobias Rydén
Hidden Markov models (HMMs) are probabilistic functions of finite Markov chains, or, put in other words, state space models with finite state space. In this paper, we examine subsp…
Fast simulated annealing in and an application to maximum likelihood estimation
Sylvain Rubenthaler, Tobias Rydén, Magnus Wiktorsson
Using classical simulated annealing to maximise a function defined on a subset of , the probability $\p(ψ(θ\_n)\leq ψ\_{\max}-ε)$ tends to zero at a logarithmic rate as $…
Nonparametric estimation of mixing densities for discrete distributions
François Roueff, Tobias Rydén
By a mixture density is meant a density of the form , where is a family of probability densities and is a probability meas…
Hidden Markov and state space models: asymptotic analysis of exact and approximate methods for prediction, filtering, smoothing and statistical inference
Peter Bickel, Yaacov Ritov, Tobias Rydén
State space models have long played an important role in signal processing. The Gaussian case can be treated algorithmically using the famous Kalman filter. Similarly since the 197…