The following pages link to mclust (Q13318):
Displaying 50 items.
- Parsimonious Bayesian factor analysis for modelling latent structures in spectroscopy data (Q2080755) (← links)
- Estimation of the complexity of a finite mixture distribution: from well- to less known methods (Q2081736) (← links)
- Bayesian mixture model of extended redundancy analysis (Q2088918) (← links)
- How many data clusters are in the galaxy data set? Bayesian cluster analysis in action (Q2089292) (← links)
- Factor and hybrid components for model-based clustering (Q2089297) (← links)
- Gaussian mixture model with an extended ultrametric covariance structure (Q2089298) (← links)
- A mixture model approach to spectral clustering and application to textual data (Q2111310) (← links)
- Robust clustering of multiply censored data via mixtures of \(t\) factor analyzers (Q2125473) (← links)
- Chimeral clustering (Q2129309) (← links)
- High-dimensional clustering via random projections (Q2129311) (← links)
- Multivariate cluster-weighted models based on seemingly unrelated linear regression (Q2129595) (← links)
- Tensor decomposition for learning Gaussian mixtures from moments (Q2133926) (← links)
- Computing highest density regions for continuous univariate distributions with known probability functions (Q2135881) (← links)
- Learning the smoothness of noisy curves with application to online curve estimation (Q2136651) (← links)
- Smallest covering regions and highest density regions for discrete distributions (Q2155016) (← links)
- Robust fitting of mixture models using weighted complete estimating equations (Q2157525) (← links)
- Determine the number of clusters by data augmentation (Q2161184) (← links)
- The spatial representation of consumer dispersion patterns via a new multi-level latent class methodology (Q2169868) (← links)
- On assessments of agreement between fuzzy partitions (Q2169878) (← links)
- Gaussian mixture models based on principal components and applications (Q2193297) (← links)
- Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics (Q2195280) (← links)
- Mini-batch learning of exponential family finite mixture models (Q2195820) (← links)
- Vertex nomination, consistent estimation, and adversarial modification (Q2199707) (← links)
- A robust approach to model-based classification based on trimming and constraints. Semi-supervised learning in presence of outliers and label noise (Q2201323) (← links)
- Infinite mixtures of infinite factor analysers (Q2226717) (← links)
- Model-based feature selection and clustering of RNA-seq data for unsupervised subtype discovery (Q2233190) (← links)
- An evolutionary algorithm with crossover and mutation for model-based clustering (Q2236771) (← links)
- Using projection-based clustering to find distance- and density-based clusters in high-dimensional data (Q2236772) (← links)
- In the pursuit of sparseness: a new rank-preserving penalty for a finite mixture of factor analyzers (Q2242013) (← links)
- Infinite Dirichlet mixture models learning via expectation propagation (Q2256784) (← links)
- Model-based SIR for dimension reduction (Q2275652) (← links)
- Investigation of parameter uncertainty in clustering using a Gaussian mixture model via jackknife, bootstrap and weighted likelihood bootstrap (Q2282602) (← links)
- Growth mixture modeling with measurement selection (Q2283309) (← links)
- Determinantal point process mixtures via spectral density approach (Q2297238) (← links)
- Weighted likelihood mixture modeling and model-based clustering (Q2302489) (← links)
- Supervised learning via smoothed Polya trees (Q2303053) (← links)
- Finite mixture-of-gamma distributions: estimation, inference, and model-based clustering (Q2303063) (← links)
- Vertex nomination: the canonical sampling and the extended spectral nomination schemes (Q2305315) (← links)
- Mixtures of multivariate contaminated normal regression models (Q2306894) (← links)
- Model-based clustering with sparse covariance matrices (Q2329799) (← links)
- Zero-inflated regime-switching stochastic differential equation models for highly unbalanced multivariate, multi-subject time-series data (Q2331187) (← links)
- Fast multivariate log-concave density estimation (Q2337320) (← links)
- Outlier identification in model-based cluster analysis (Q2353146) (← links)
- Testing over-representation of observations in subsets of a DEA technology (Q2355920) (← links)
- Non-parametric clustering over user features and latent behavioral functions with dual-view mixture models (Q2358914) (← links)
- Variational Bayesian inference for the latent position cluster model for network data (Q2359518) (← links)
- Multivariate response and parsimony for Gaussian cluster-weighted models (Q2359572) (← links)
- Better alternatives to current methods of scaling and weighting data for cluster analysis (Q2382873) (← links)
- Clustering via minimum volume ellipsoids (Q2385541) (← links)
- Robust clustering in regression analysis via the contaminated Gaussian cluster-weighted model (Q2403302) (← links)
