Dynamic model-based clustering for spatio-temporal data

DOI10.1007/s11222-017-9735-9zbMath1384.62201OpenAlexW2590642040MaRDI QIDQ1702014

Lucia Paci, Francesco Finazzi

Publication date: 27 February 2018

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11222-017-9735-9

zbMATH Keywords

Bayesian analysisMarkov chain Monte Carlofinite mixture modelsstate-space modeling

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15)

Related Items (1)

New formulation of the logistic-Gaussian process to analyze trajectory tracking data

Uses Software

• BayesLogit
• spBayes

Cites Work

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• Model-based clustering based on sparse finite Gaussian mixtures
• Methods for merging Gaussian mixture components
• Spatial product partition models
• Bayesian forecasting and dynamic models.
• A Bayesian nonparametric approach for time series clustering
• A multivariate semiparametric Bayesian spatial modeling framework for hurricane surface wind fields
• A practical approach for assessing the effect of grouping in hierarchical spatio-temporal models
• Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling
• Finite mixture and Markov switching models.
• Model based clustering for three-way data structures
• The Dirichlet labeling process for clustering functional data
• Generalized Spatial Dirichlet Process Models
• Model choice: a minimum posterior predictive loss approach
• Conditional Prior Proposals in Dynamic Models
• Dealing With Label Switching in Mixture Models
• Mixture model with multiple allocations for clustering spatially correlated observations in the analysis of ChIP‐Seq data
• Bayesian Measures of Model Complexity and Fit
• Modelling Spatially Correlated Data via Mixtures: A Bayesian Approach
• Cluster-based network model for time-course gene expression data
• Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables
• Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics
• Bayesian Nonparametric Spatial Modeling With Dirichlet Process Mixing
• Deviance information criteria for missing data models
• Space‐time areal mixture model: relabeling algorithm and model selection issues

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