Showing items with similar title or keywords.

• On resampling schemes for particle filters with weakly informative observations 

  Chopin, Nicolas; Singh, Sumeetpal S.; Soto, Tomás; Vihola, Matti (Institute of Mathematical Statistics, 2022)

  We consider particle filters with weakly informative observations (or ‘potentials’) relative to the latent state dynamics. The particular focus of this work is on particle filters to approximate time-discretisations of ...

• Importance sampling correction versus standard averages of reversible MCMCs in terms of the asymptotic variance 

  Franks, Jordan; Vihola, Matti (Elsevier, 2020)

  We establish an ordering criterion for the asymptotic variances of two consistent Markov chain Monte Carlo (MCMC) estimators: an importance sampling (IS) estimator, based on an approximate reversible chain and subsequent ...

• Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data 

  Helske, Satu; Helske, Jouni; Eerola, Mervi (Springer, 2018)

  Life course data often consists of multiple parallel sequences, one for each life domain of interest. Multichannel sequence analysis has been used for computing pairwise dissimilarities and finding clusters in this type ...

• Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo 

  Vihola, Matti; Helske, Jouni; Franks, Jordan (Wiley-Blackwell, 2020)

  We consider importance sampling (IS) type weighted estimators based on Markov chain Monte Carlo (MCMC) targeting an approximate marginal of the target distribution. In the context of Bayesian latent variable models, the ...

• Cluster priors in the Bayesian modelling of fMRI data 

  Taskinen, Ilkka (University of Jyväskylä, 2001)