Zhou, "Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models," Nature Communications, vol. 8, no. 1, p. 456, 2017.Zeng P, Zhou X. Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models. Nature Communications...
Nature Communications, 8(1): 456, Doi: 10.1038/s41467-017-00470-2. Contact We are very grateful to any questions, comments, or bugs reports; and please contact Ping Zeng via zpstat@xzhmu.edu.cn or pingzeng@umich.edu.About DPR: latent Dirichlet Process Regression for Genetic Prediction ...
1. Theoretical Intro Generally speaking, Hierarchical Dirichlet Process, when integrated into LDA in topic modelling, consists of three-level Dirichlet Processes. To directly depict its generative process, I drew two rough graphical models as below: Figure 1: HDP Figure 2: LDA The circles represent...
Local Dirichlet processMeta-regressionSpatial dependencyTwo-stage meta-analysis has been popularly used in epidemiological studies to investigate an association ... Yu, JaeeunPark, JinsuChoi, TaeryonHashizume, MasahiroKim, YoonheeHonda, YasushiChung, Yeonseung - 《Journal of Agricultural Biological & Env...
Summary: Consider the problem of learning logistic-regression models for multiple classification tasks, where the training data set for each task is not drawn from the same statistical distribution. In such a multi-task learning (MTL) scenario, it is necessary to identify groups of similar tasks ...
Through extensive simulations, we compare the per-formance of our proposed method with that of commonly used nonparametric regression techniques. We conclude that when the model assumption holds and the subpopulation are not highly overlapping, our method has smaller estimation error particularly if the...
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I am trying to use the predicted probabilities from a multinomial regression using multinom function from the nnet package in R to estimate the parameters for a Dirichlet distribution while taking into account the standard errors. Below is an example of what I'm trying to do. I have two sets...
DirichletReg: Dirichlet Regression for Compositional Data in R Dirichlet regression models can be used to analyze a set of variables lying in a bounded interval that sum up to a constant (e.g., proportions, rates, compositions, etc.) exhibiting skewness and heteroscedasticity, without having to ...
In DSA model, Logistic regression is second-hand for the binary classification difficulty. But in this DSA model some other categories of the sentiment classification such as intermediary and subjunctive reversed reviews are not maintained, to conquer this problem in this work proposed a new method ...