Mixture models : inference and applications to clustering

By Geoffrey J. McLachlan, K. Basford. 1989

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Citations in the corpus

Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-to-End Learning from Demonstration (2017, methodology): “We do not calculate the full covariance matrices for each component, since this form of Gaussian mixture model is general enough to approximate any density function [38].”
Estimating Optical Flow in Segmented Images Using Variable-Order Parametric Models With Local Deformations (1996, methodology): “The objective function is minimized using an algorithm similar to the EM-algorithm [34] in which at each iteration we solve for the ms and ls;t in closed form and then update the is using one step of Newton’s method.”
An Introduction to Applied Multivariate Analysis with R (2011, methodology): “1 Finite mixture densities Finite mixture densities are described in detail in Everitt and Hand (1981), Titterington, Smith, and Makov (1985), McLachlan and Basford (1988), McLachlan and Peel (2000), and Frühwirth-Schnatter (2006); they are a family of probability density functions of the form”
Large-scale clinical interpretation of genetic variants using evolutionary data and deep learning (2020, background): “The Gaussian Mixture Model (GMM) [24] is a probabilistic model that assumes the data are generated from a mixture of a finite number of Gaussian distributions with unknown parameters.”
Using unlabelled data to update classification rules with applications in food authenticity studies (2006, background): “General reviews of the topic of updating classification rules by using unlabelled data are given in Titterington et al. (1985), section 5.7, McLachlan and Basford (1988), section 1.11, McLachlan (1992), section 2.6, and McLachlan and Peel (2000), section 2.19.”
Entropies and rates of convergence for maximum likelihood and Bayes estimation for mixtures of normal densities (2001, methodology): “Normal mixture models have been used for a variety of inference problems including density estimation, clustering analysis and robust estimation; see, for example, Lindsay (1995), McLachlan and Basford (1988), Banfield and Raftery (1993), Robert (1996) and Roeder and Wasserman (1997).”
A review of novelty detection (2014, methodology): “More complex forms of data distribution may be modelled by mixture models such as GMMs [34,103], or other mixtures of different types of distributions such as the gamma [104,105], the Poisson [106], the Student0s t [107], and the Weibull [108] distributions.”
Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines (2021, methodology): “With the generated model, newly added time-series data can be inspected to determine whether it belongs to the normal boundary [78].”
A Split-Merge Markov chain Monte Carlo Procedure for the Dirichlet Process Mixture Model (2004, methodology): “Mixture models are often applied to density estimation, latent class analysis, and classi cation problems, as discussed, for example, by Everitt and Hand (1981), McLachlan and Basford (1988), and Titterington,Smith, and Makov (1985).”
Inferring age from otolith measurements: a review and a new approach (2004, methodology): “In its most general form, mixture analysis can present some technical problems (McLachlan and Basford 1988).”
Unsupervised pattern discovery in electronic health care data using probabilistic clustering models (2012, background): “By contrast, probabilistic clustering models, also referred to as mixture models [15], can deal with missing data very efficiently under certain assumptions [4].”
Comparing clusterings---an information based distance (2007, background): “The basic properties of VI are presented and discussed.”
Learning Mixture Models of Spatial Coherence (1993, methodology): “One way to handle multiple models is to have a mixture of distributions ( McLachlan and Basford 1988 ).”