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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 ... -
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 ... -
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 ... -
Unbiased Inference for Discretely Observed Hidden Markov Model Diffusions
Chada, Neil K.; Franks, Jordan; Jasra, Ajay; Law, Kody J.; Vihola, Matti (Society for Industrial & Applied Mathematics (SIAM), 2021)We develop a Bayesian inference method for diffusions observed discretely and with noise, which is free of discretization bias. Unlike existing unbiased inference methods, our method does not rely on exact simulation ... -
Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests
Kuronen, Mikko; Särkkä, Aila; Vihola, Matti; Myllymäki, Mari (Springer Science and Business Media LLC, 2022)We propose a hierarchical log Gaussian Cox process (LGCP) for point patterns, where a set of points xx affects another set of points yy but not vice versa. We use the model to investigate the effect of large trees on the ...