Results 11 to 20 of about 8,492,854 (308)
Open, online, interactive visualizations for learning about Gaussian mixture models [PDF]
We construct a website to explain how Gaussian mixture models can be optimized using the expectation maximization algorithm. Previous free, online material on this process has been extremely limited.
Allen, Stuart
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Graph Laplacian Mixture Model [PDF]
IEEE Transactions on Signal and Information Processing over Networks, 2020 Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets. Most of the recent approaches focus on different relationships between a graph and data sample distributions, mostly in settings where all available data relate to the same graph.
Hermina Petric Maretic, Pascal Frossard
openaire +4 more sources
On Choosing a Mixture Model for Clustering [PDF]
Journal of Data Science, 2021 2 Universit e de Caen Abstract: Two methods for clustering data and choosing a mixture model are proposed. First, we derive a new classication algorithm based on the classication likelihood. Then, the likelihood conditional on these clusters is written as the product of likelihoods of each cluster, and AIC- respectively BIC-type approximations are ...
Ngatchou Wandji, Joseph, Bulla, Jan
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A family of mixture models for biclustering [PDF]
Statistical Analysis and Data Mining: The ASA Data Science Journal, 2021 AbstractBiclustering is used for simultaneous clustering of the observations and variables when there is no group structure known a priori. It is being increasingly used in bioinformatics, text analytics, and so on. Previously, biclustering has been introduced in a model‐based clustering framework by utilizing a structure similar to a mixture of factor
Wangshu Tu, Sanjeena Subedi
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flexCWM: A Flexible Framework for Cluster-Weighted Models
Journal of Statistical Software, 2018 Cluster-weighted models (CWMs) are mixtures of regression models with random covariates. However, besides having recently become rather popular in statistics and data mining, there is still a lack of support for CWMs within the most popular statistical ...
Angelo Mazza +2 more
doaj +1 more source
IEEE Access, 2018 The destructive effects of cyber-attacks demand more proactive security approaches. One such promising approach is the idea of collaborative intrusion detection systems (CIDSs).
Carlos Garcia Cordero +6 more
doaj +1 more source
Deep Gaussian mixture models [PDF]
Statistics and Computing, 2017 Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, Deep Gaussian Mixture Models are introduced and discussed. A Deep Gaussian Mixture model (DGMM) is a network of multiple layers of latent variables, where, at each layer, the variables ...
Cinzia Viroli, Geoffrey J. McLachlan
openaire +9 more sources
Computational aspects of N-mixture models [PDF]
, 2015 The N-mixture model is widely used to estimate the abundance of a population in the presence of unknown detection probability from only a set of counts subject to spatial and temporal replication (Royle, 2004, Biometrics 60,105–115).
Morgan, Byron J. T. +3 more
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How to fit models of recognition memory data using maximum likelihood.
International Journal of Psychological Research, 2010 The aim of this paper is to provide an introductory tutorial to how to fit different models of recognition memory using maximum likelihood estimation. It is in four main parts.
John C. Dunn
doaj +1 more source
Perfect posterior simulation for mixture and hidden Markov models [PDF]
, 2010 In this paper we present an application of the read-once coupling from the past algorithm to problems in Bayesian inference for latent statistical models.
Berthelsen, Kasper Klitgaard +6 more
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