A Monte Carlo simulation was performed to compare methods for handling missing data in growth mixture models. The considered the current study were (a) a fully Bayesian approach using Gibbs sampler, (b) full information maximum likelihood expectation–maximization algorithm, (c) multiple imputation, (d) two-stage imputation method, and (e) listwise deletion. Of five methods, it found that genera...

Inspired by the analysis of variance (ANOVA) decomposition functions, we propose a Gaussian-uniform mixture model on high-dimensional torus which relies assumption that function wish to approximate can be well explained limited variable interactions. We consider three approaches, namely wrapped Gaussians, diagonal and products von Mises distributions. The sparsity is ensured fact its summands a...

We propose a learning-based method for light-path construction in path tracing algorithms, which iteratively optimizes and samples from what we refer to as spatio-directional Gaussian mixture models (SDMMs). In particular, approximate incident radiance an online-trained 5D that is accelerated by D-tree. Using the same framework, BSDFs pre-trained D mixtures, where number of BSDF parameters. Suc...

Growth mixture modeling has gained much attention in applied and methodological social science research recently, but the selection of the number of latent classes for such models remains a challenging issue, especially when the assumption of proper model specification is violated. The current simulation study compared the performance of a linear growth mixture model (GMM) for determining the c...

In order to achieve the maximum flexibility in adaptation to ever changing customer’s expectations in customer relationship management, appropriate measures of customer behavior should be continually monitored. To this end, control charts adjusted for buyer’s/visitor’s prior intention to repurchase or visit again are suitable means taking into account the heterogeneity across customers. In the ...

The finite mixture model is widely used in various statistical learning problems. However, the model obtained may contain a large number of components, making it inefficient in practical applications. In this paper, we propose to simplify the mixture model by first grouping similar components together and then performing local fitting through function approximation. By using the squared loss to...

Teaching of machine learning should aim at the readiness to understand and implement modern machine learning algorithms. Towards this goal, we often have course exercises involving the student to solve a practical machine learning problem involving a reallife data set. The students implement the programs of machine learning methods themselves and gain deep insight on the implementation details ...