Showing items with similar title or keywords.

• 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 ...

• 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 ...

• On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction 

  Vihola, Matti; Franks, Jordan (Oxford University Press, 2020)

  Approximate Bayesian computation enables inference for complicated probabilistic models with intractable likelihoods using model simulations. The Markov chain Monte Carlo implementation of approximate Bayesian computation ...

• Conditional particle filters with diffuse initial distributions 

  Karppinen, Santeri; Vihola, Matti (Springer, 2021)

  Conditional particle filters (CPFs) are powerful smoothing algorithms for general nonlinear/non-Gaussian hidden Markov models. However, CPFs can be inefficient or difficult to apply with diffuse initial distributions, which ...

• Theoretical and methodological aspects of MCMC computations with noisy likelihoods 

  Andrieu, Christophe; Lee, Anthony; Vihola, Matti (Chapman and Hall/CRC, 2018)

  Approximate Bayesian computation (ABC) [11, 42] is a popular method for Bayesian inference involving an intractable, or expensive to evaluate, likelihood function but where simulation from the model is easy. The method ...