Finite mixture models are being used increasingly to model a wide variety of random
phenomena for clustering, classification and density estimation. mclust is a powerful and …

As an algorithm with excellent performance, convolutional neural network has been widely
used in the field of image processing and achieved good results by relying on its own local …

This open access book discusses the statistical modeling of insurance problems, a process
which comprises data collection, data analysis and statistical model building to forecast …

Model-Based Clustering, Classification, and Denisty Estimation Using mclust in R Model-
based clustering and classification methods provide a systematic statistical approach to …

In this paper, we formulate and estimate a flexible model of job mobility and wages with two‐
sided heterogeneity. The analysis extends the finite mixture approach of Bonhomme …

Evidential clustering is an approach to clustering in which cluster-membership uncertainty is
represented by a collection of Dempster-Shafer mass functions forming an evidential …

Mixture of experts (MoE) is a popular framework in the fields of statistics and machine
learning for modeling heterogeneity in data for regression, classification and clustering. MoE …

在震源参数反演理论研究中, 地表形变与震源参数之间为复杂多维的非线性关系,
针对传统泰勒级数展开的精度评定方法可能无法适用于震源参数的精度评定问题 …

Heterogeneity has been a hot topic in recent educational literature. Several calls have been
voiced to adopt methods that capture different patterns or subgroups within students …

Most autosomal genes are thought to be expressed from both alleles, with some notable
exceptions, including imprinted genes and genes showing random monoallelic expression …