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Spatial clustering of time series via mixture of autoregressions models and Markov random fields
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Discriminant Analysis and Statistical Pattern Recognition
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Spatial mixture modeling of fMRI data
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The EM Algorithm and Extensions, 2E
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Seamless R and C++ Integration with Rcpp
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A maximum likelihood methodology for clusterwise linear regression
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Enhanced Model-Based Clustering, Density Estimation, and Discriminant Analysis Software: MCLUST
inferredcites →https://doi.org/10.1007/s10182-008-0060-7openalex-snapshot
On composite marginal likelihoods
inferredcites →https://doi.org/10.1007/s11634-011-0096-5openalex-snapshot
Model-based clustering and segmentation of time series with changes in regime
inferredcites →https://doi.org/10.1007/s11634-015-0209-7openalex-snapshot
Maximum likelihood estimation of Gaussian mixture models without matrix operations
inferredcites →https://doi.org/10.1016/j.jspi.2005.12.014openalex-snapshot
On the consistency of MLE in finite mixture models of exponential families
inferredcites →https://doi.org/10.1016/j.patcog.2003.12.018openalex-snapshot
Time series clustering with ARMA mixtures
inferredcites →https://doi.org/10.1016/j.patcog.2005.01.025openalex-snapshot
Clustering of time series data—a survey
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Generic Uniform Convergence
inferredcites →https://doi.org/10.1093/bioinformatics/btg014openalex-snapshot
Clustering of time-course gene expression data using a mixed-effects model with B-splines
inferredcites →https://doi.org/10.1093/bioinformatics/btl165openalex-snapshot
A Mixture model with random-effects components for clustering correlated gene-expression profiles
inferredcites →https://doi.org/10.1093/bioinformatics/btp686openalex-snapshot
Mixtures of regression models for time course gene expression data: evaluation of initialization and random effects
inferredcites →https://doi.org/10.1093/biomet/71.1.1openalex-snapshot
On the Existence of Maximum Likelihood Estimates in Logistic Regression Models
inferredcites →https://doi.org/10.1109/tmi.2004.836545openalex-snapshot
Mixture models with adaptive spatial regularization for segmentation with an application to FMRI data
inferredcites →https://doi.org/10.1109/tmi.2010.2042064openalex-snapshot
Spatially Adaptive Mixture Modeling for Analysis of fMRI Time Series
inferredcites →https://doi.org/10.1109/tmi.2014.2322369openalex-snapshot
False Discovery Rate Control in Magnetic Resonance Imaging Studies via Markov Random Fields
inferredcites →https://doi.org/10.1109/tpami.2002.1046170openalex-snapshot
Approximate Bayes factors for image segmentation: the Pseudolikelihood Information Criterion (PLIC)
inferredcites →https://doi.org/10.1111/j.1467-842x.1992.tb01356.xopenalex-snapshot
FITTING FINITE MIXTURE MODELS IN A REGRESSION CONTEXT
inferredcites →https://doi.org/10.1111/j.2517-6161.1986.tb01412.xopenalex-snapshot
On the Statistical Analysis of Dirty Pictures
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A Unified Convergence Analysis of Block Successive Minimization Methods for Nonsmooth Optimization
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A general probabilistic framework for clustering individuals and objects
inferredcites →https://doi.org/10.1186/1471-2105-13-300openalex-snapshot
Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects
inferredcites →https://doi.org/10.1191/1471082x05st096oaopenalex-snapshot
Mixture of linear mixed models for clustering gene expression profiles from repeated microarray experiments
inferredcites →https://doi.org/10.1198/016214502760047131openalex-snapshot
Model-Based Clustering, Discriminant Analysis, and Density Estimation
inferredcites →https://doi.org/10.1198/016214502760301200openalex-snapshot
Asymptotic Theory for Econometricians
inferredcites →https://doi.org/10.1214/aoap/1034968138openalex-snapshot
A consistent model selection procedure for Markov random fields based on penalized pseudolikelihood
inferredcites →https://doi.org/10.1214/aos/1176344136openalex-snapshot
Estimating the Dimension of a Model
inferredcites →https://doi.org/10.1523/jneurosci.2601-12.2012openalex-snapshot
Optimization of a GCaMP Calcium Indicator for Neural Activity Imaging
inferredcites →https://doi.org/10.18637/jss.v004.i02openalex-snapshot
The EMMIX Algorithm for the Fitting of Normal and t -Components
inferredcites →https://doi.org/10.2307/2348072openalex-snapshot
Mixture Models: Inference and Applications to Clustering.
inferredcites →https://doi.org/10.2307/2531224openalex-snapshot
Statistical Analysis of Finite Mixture Distributions.
inferredcites →https://doi.org/10.3389/fncir.2013.00001openalex-snapshot
Error detection and representation in the olivo-cerebellar system
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Prey capture in zebrafish larvae serves as a model to study cognitive functions
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Clustering complex time‐series databases by using periodic components
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Whole‐volume clustering of time series data from zebrafish brain calcium images via mixture modeling
inferred← citeshttps://doi.org/10.1002/widm.1198openalex-snapshot
An introduction to Majorization‐Minimization algorithms for machine learning and statistical estimation
inferred← citeshttps://doi.org/10.1002/widm.1298openalex-snapshot
Model‐based clustering and classification of functional data
inferred← citeshttps://doi.org/10.1007/s11634-024-00580-yopenalex-snapshot
Spatial quantile clustering of climate data
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A Mixture Model Approach for Compositional Data: Inferring Land-Use Influence on Point-Referenced Water Quality Measurements
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Robust clustering of COVID-19 cases across U.S. counties using mixtures of asymmetric time series models with time varying and freely indexed covariates
inferred← citeshttps://doi.org/10.1111/stan.12315openalex-snapshot
An efficient automatic clustering algorithm for probability density functions and its applications in surface material classification
inferred← citeshttps://doi.org/10.1162/neco_a_00938openalex-snapshot
Maximum Pseudolikelihood Estimation for Model-Based Clustering of Time Series Data
inferred← citeshttps://doi.org/10.48550/arxiv.1611.03969openalex-snapshot
An Introduction to MM Algorithms for Machine Learning and Statistical
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