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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
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Modeling Learner Heterogeneity: A Mixture Learning Model With Responses and Response Times
Frontiers in Psychology, 2018 The increased popularity of computer-based testing has enabled researchers to collect various types of process data, including test takers' reaction time to assessment items, also known as response times. In recent studies, the relationship between speed
Susu Zhang, Shiyu Wang
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Improved Initialization of the EM Algorithm for Mixture Model Parameter Estimation
Mathematics, 2020 A commonly used tool for estimating the parameters of a mixture model is the Expectation−Maximization (EM) algorithm, which is an iterative procedure that can serve as a maximum-likelihood estimator.
Branislav Panić +2 more
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Model‐based clustering reveals patterns in central place use of a marine top predator
Ecosphere, 2020 Satellite telemetry data are commonly used to quantify habitat selection, examine animal movements, and delineate home ranges. These data also contain valuable information concerning dens, nests, roosts, and other central places that are often associated
Brian M. Brost +2 more
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Energies, 2019 A combination of two passive heat transfer enhancement techniques using a microfin structure and nanofluids was investigated numerically. TiO2/water nanofluids flowing inside a square minichannel with a microfin structure (SMM) were observed as a ...
Budi Kristiawan +4 more
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Frontiers in Genetics, 2020 Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is an important experimental method for detecting specific protein-mediated chromatin loops genome-wide at high resolution. Here, we proposed a new statistical approach with a mixture
Yibeltal Arega +6 more
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Mathematics, 2022 The need to model proportional data is common in a range of disciplines however, due to its bimodal nature, U- or J-shaped data present a particular challenge.
Janette Larney +2 more
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Human Vaccines & Immunotherapeutics, 2018 A single dose of the measles-mumps-rubella (MMR) vaccine has been applied in routine immunizations for children in China; however, the Immunoglobulin G (IgG) antibody level of mumps in children from kindergarten to early school age with MMR vaccine ...
Lei Zhang +8 more
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Generalized Analysis of a Distribution Separation Method
Entropy, 2016 Separating two probability distributions from a mixture model that is made up of the combinations of the two is essential to a wide range of applications. For example, in information retrieval (IR), there often exists a mixture distribution consisting of
Peng Zhang +5 more
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, 2019 Mixtures of experts models provide a framework in which covariates may be included in mixture models. This is achieved by modelling the parameters of the mixture model as functions of the concomitant covariates. Given their mixture model foundation, mixtures of experts models possess a diverse range of analytic uses, from clustering observations to ...
Gormley, Isobel Claire +1 more
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