G5 Doctoral dissertation (article)
Non-linear state-space methods for Bayesian time series modelling (2022)
Epälineaarisia tila-avaruusmenetelmiä Bayes-aikasarjamallinnukseen

Karppinen, S. (2022). Non-linear state-space methods for Bayesian time series modelling [Doctoral dissertation]. Jyväskylän yliopisto. JYU dissertations, 572. http://urn.fi/URN:ISBN:978-951-39-9226-2

JYU authors or editors

Publication details

All authors or editorsKarppinen, Santeri


Journal or seriesJYU dissertations


Publication year2022

Number in series572

Number of pages in the book1 verkkoaineisto (51 sivua, 88 sivua useina numerointijaksoina, 4 numeroimatonta sivua)

PublisherJyväskylän yliopisto

Place of PublicationJyväskylä

Publication countryFinland

Publication languageEnglish

Persistent website addresshttp://urn.fi/URN:ISBN:978-951-39-9226-2

Publication open accessOpenly available

Publication channel open accessOpen Access channel


State-space methods are used in many fields of science to solve so called filtering, smoothing, prediction and parameter inference problems using multivariate time series data. Analytical solutions to these inference problems exist mainly for linear Gaussian state-space models and discrete state-space models. Outside these special cases, the inference is typically based on approximate methods, or simulation-based methods such as particle filters. This thesis develops new methods for Bayesian inference of general state- space models and applies existing methods in challenging non-linear problems involving multivariate time series data. The new methods presented in this thesis are conditional particle filters that are relevant for the inference of models that involve uninformative initial state distributions and models that have slowly-mixing state dynamics and/or weakly informative observation processes. The applied problems develop new non-linear state-space models in order to solve a prediction problem related to childhood acute lymphoblastic leukaemia and a filtering problem related to the identification of wolf territories based on presence-only citizen science data.

Keywordstime seriestime-series analysisMarkov chainsMonte Carlo methodsBayesian analysisdoctoral dissertations

Free keywordsnon-linear; state-space model; particle filter; Bayesian inference; Feynman–Kac model; Markov chain; Monte Carlo

Contributing organizations

Related projects

Ministry reportingYes

Reporting Year2022

Last updated on 2024-30-04 at 20:06