Minimum Description Length Based Hidden Markov Model Clustering for Life Sequence Analysis
Helske, J., Eerola, M., & Tabus, I. (2010). Minimum Description Length Based Hidden Markov Model Clustering for Life Sequence Analysis. In Workshop on Information Theoretic Methods in Science and Engineering. http://sp.cs.tut.fi/WITMSE10/Proceedings/index.html
Päivämäärä
2010Tekijänoikeudet
© The Authors 2010
In this article, a model-based method for clustering life
sequences is suggested. In the social sciences, model-free
clustering methods are often used in order to find typical
life sequences. The suggested method, which is based on
hidden Markov models, provides principled probabilistic
ranking of candidate clusterings for choosing the best solution. After presenting the principle of the method and
algorithm, the method is tested with real life data, where
it finds eight descriptive clusters with clear probabilistic
structures.
Kuuluu julkaisuun
Workshop on Information Theoretic Methods in Science and Engineering
Alkuperäislähde
http://sp.cs.tut.fi/WITMSE10/Proceedings/index.htmlJulkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/20099158
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Lisenssi
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
Statistical analysis of life sequence data
Helske, Satu (University of Jyväskylä, 2016) -
Analysing Complex Life Sequence Data with Hidden Markov Modelling
Helske, Satu; Helske, Jouni; Eerola, Mervi (LIVES - Swiss National Centre of Competence in Research; Swiss National Science Foundation; Université de Genevè, 2016)When analysing complex sequence data with multiple channels (dimensions) and long observation sequences, describing and visualizing the data can be a challenge. Hidden Markov models (HMMs) and their mixtures (MHMMs) ... -
Dynamic Community Detection for Brain Functional Networks during Music Listening with Block Component Analysis
Zhu, Yongjie; Liu, Jia; Cong, Fengyu (IEEE, 2023)The human brain can be described as a complex network of functional connections between distinct regions, referred to as the brain functional network. Recent studies show that the functional network is a dynamic process ... -
Cluster priors in the Bayesian modelling of fMRI data
Taskinen, Ilkka (University of Jyväskylä, 2001) -
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 ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.