Bayesian unsupervised classification framework based on stochastic partitions of data and a parallel search strategy

DOI10.1007/s11634-009-0036-9zbMath1231.62031OpenAlexW2101630451MaRDI QIDQ734517

Mats Gyllenberg, Jukka Corander, Timo Koski

Publication date: 13 October 2009

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-83214

zbMATH Keywords

stochastic optimizationMarkov chain Monte CarloBayesian classificationstatistical learning

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Bayesian inference (62F15) Inference from stochastic processes (62M99) Statistical decision theory (62C99) Numerical analysis or methods applied to Markov chains (65C40) Artificial intelligence (68T99)

Related Items

Simultaneous predictive Gaussian classifiers, Bayesian model learning based on predictive entropy, Bayesian analysis of population structure based on linked molecular information, Random partition models and exchangeability for Bayesian identification of population struc\-ture, Learning genetic population structures using minimization of stochastic complexity, Have I seen you before? Principles of Bayesian predictive classification revisited, Computing exact clustering posteriors with subset convolution, Replica analysis of Bayesian data clustering

Uses Software

• BAPS 2

Cites Work

• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Bayesian analysis of population structure based on linked molecular information
• W. E. Johnson's sufficientness postulate
• Nonstationary Markov chains and convergence of the annealing algorithm
• Probabilistic models in cluster analysis
• Combinatorial structures and structures for classification
• Classification of binary vectors by stochastic complexity.
• Bayesian network classifiers
• Bayesian predictiveness, exchangeability and sufficientness in bacterial taxonomy
• TAN classifiers based on decomposable distributions
• Random partition models and exchangeability for Bayesian identification of population struc\-ture
• Finite Markov Chains and Algorithmic Applications
• Probabilistic Models for Bacterial Taxonomy
• Fisher information and stochastic complexity
• Transdimensional Markov Chains
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item