Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Matti Vihola"'
Publikováno v:
SIAM/ASA Journal on Uncertainty Quantification. 9:763-787
We develop a Bayesian inference method for diffusions observed discretely and with noise, which is free of discretisation bias. Unlike existing unbiased inference methods, our method does not rely on exact simulation techniques. Instead, our method u
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 continuous-time Feynman--Kac pa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::20e22b318100ca3646c34c7cf344ef5c
http://arxiv.org/abs/2203.10037
http://arxiv.org/abs/2203.10037
Publikováno v:
Scandinavian Journal of Statistics. 47:1339-1376
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 MCMC typically operates on the
Citizens, community groups and local institutions participate in voluntary biological monitoring of population status and trends by providing species data e.g. for regulations and conservation. Sophisticated statistical methods are required to unlock
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a12d419699917090b1ee382c0e4fa2ea
http://urn.fi/URN:NBN:fi:jyu-202209014438
http://urn.fi/URN:NBN:fi:jyu-202209014438
Autor:
Jouni Helske, Matti Vihola
We present an R package bssm for Bayesian non-linear/non-Gaussian state space modelling. Unlike the existing packages, bssm allows for easy-to-use approximate inference based on Gaussian approximations such as the Laplace approximation and the extend
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f6d192bdef2acf4a38f587c4e222c45b
http://arxiv.org/abs/2101.08492
http://arxiv.org/abs/2101.08492
Autor:
Matti Vihola, Santeri Karppinen
Publikováno v:
Statistics and Computing
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 are common in statistical
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::20751a90d0df394f233d0fd2576f99c2
http://arxiv.org/abs/2006.14877
http://arxiv.org/abs/2006.14877
We propose a hierarchical log Gaussian Cox process (LGCP) for point patterns, where a set of points x affects another set of points y but not vice versa. We use the model to investigate the effect of large trees to the locations of seedlings. In the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::578b0801497be76ec0c49bc2de7fc48d
Publikováno v:
Ann. Statist. 48, no. 5 (2020), 3066-3089
The conditional particle filter (CPF) is a promising algorithm for general hidden Markov model smoothing. Empirical evidence suggests that the variant of CPF with backward sampling (CBPF) performs well even with long time series. Previous theoretical
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9083d7121eda0d5a61293c7047dbfca2
https://www.repository.cam.ac.uk/handle/1810/298799
https://www.repository.cam.ac.uk/handle/1810/298799
Autor:
Matti Vihola
Publikováno v:
Operations Research. 66:448-462
Multilevel Monte Carlo (MLMC) and unbiased estimators recently proposed by McLeish (Monte Carlo Methods Appl., 2011) and Rhee and Glynn (Oper. Res., 2015) are closely related. This connection is elaborated by presenting a new general class of unbiase
Autor:
Matti Vihola, Jordan Franks
Approximate Bayesian computation allows for inference of complicated probabilistic models with intractable likelihoods using model simulations. The Markov chain Monte Carlo implementation of approximate Bayesian computation is often sensitive to the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::089ebe3abb82af1bdacd0ab41755b9e9
http://arxiv.org/abs/1902.00412
http://arxiv.org/abs/1902.00412