scDAC: deep adaptive clustering of single-cell transcriptomic data with coupled autoencoder and Dirichlet process mixture model [0.03%]
基于耦合自动编码器和狄利克雷过程混合模型的单细胞转录组数据深度自适应聚类方法（SCDAC）
Sijing An,Jinhui Shi,Runyan Liu et al.
Sijing An et al.
Motivation: Clustering analysis for single-cell RNA sequencing (scRNA-seq) data is an important step in revealing cellular heterogeneity. Many clustering methods have been proposed to discover heterogenous cell types from...
Dirichlet process mixture models to impute missing predictor data in counterfactual prediction models: an application to predict optimal type 2 diabetes therapy [0.03%]
应用狄利克雷过程混合模型来填补反事实预测模型中缺失的预测数据：应用于2型糖尿病最优治疗预测
Pedro Cardoso,John M Dennis,Jack Bowden et al.
Pedro Cardoso et al.
Background: The handling of missing data is a challenge for inference and regression modelling. A particular challenge is dealing with missing predictor information, particularly when trying to build and make predictions ...
A spatio-temporal Dirichlet process mixture model for coronavirus disease-19 [0.03%]
用于冠状病毒疾病-19的时空狄利克雷过程混合模型
Jaewoo Park,Seorim Yi,Won Chang et al.
Jaewoo Park et al.
Understanding the spatio-temporal patterns of the coronavirus disease 2019 (COVID-19) is essential to construct public health interventions. Spatially referenced data can provide richer opportunities to understand the mechanism of the disea...
Dirichlet process mixture models for the analysis of repeated attempt designs [0.03%]
狄利克雷过程混合模型在重复尝试设计分析中的应用
Michael J Daniels,Minji Lee,Wei Feng
Michael J Daniels
In longitudinal studies, it is not uncommon to make multiple attempts to collect a measurement after baseline. Recording whether these attempts are successful provides useful information for the purposes of assessing missing data assumption...
Nonparametric failure time: Time-to-event machine learning with heteroskedastic Bayesian additive regression trees and low information omnibus Dirichlet process mixtures [0.03%]
非参数失效时间：具有异方差贝叶斯加性回归树和低信息 omnibus dirichlet 过程混合的时间事件机器学习
Rodney A Sparapani,Brent R Logan,Martin J Maiers et al.
Rodney A Sparapani et al.
Many popular survival models rely on restrictive parametric, or semiparametric, assumptions that could provide erroneous predictions when the effects of covariates are complex. Modern advances in computational hardware have led to an increa...
Correction to: Spike Sorting of Non-Stationary Data in Successive Intervals Based on Dirichlet Process Mixtures [0.03%]
基于狄利克雷过程混合模型在连续间隔上的非平稳数据尖峰排序的修正方案
Foozie Foroozmehr,Behzad Nazari,Saeed Sadri et al.
Foozie Foroozmehr et al.
[This corrects the article DOI: 10.1007/s11571-022-09781-7.]. © Springer Nature B.V. 2022.
Published Erratum
Cognitive neurodynamics. 2022 Dec;16(6):1407. DOI:10.1007/s11571-022-09796-0 2022
Spike Sorting of Non-Stationary Data in Successive Intervals Based on Dirichlet Process Mixtures [0.03%]
基于狄利克雷过程混合模型的连续时间间隔中非平稳数据的尖峰排序方法
Foozie Foroozmehr,Behzad Nazari,Saeed Sadri et al.
Foozie Foroozmehr et al.
This paper proposes a new automatic method for spike sorting and tracking non-stationary data based on the Dirichlet Process Mixture (DPM). Data is divided into non-overlapping intervals and mixtures are applied to individual frames rather ...
Federico Ricciardi,Silvia Liverani,Gianluca Baio
Federico Ricciardi
The regression discontinuity design is a quasi-experimental design that estimates the causal effect of a treatment when its assignment is defined by a threshold for a continuous variable. The regression discontinuity design assumes that sub...
Dynamic Dirichlet process mixture model for identifying voting coalitions in the United Nations General Assembly human rights roll call votes [0.03%]
动态狄利克雷过程混合模型在联合国大会人权问题表决中的应用以识别投票联盟
Qiushi Yu
Qiushi Yu
Scholars have been interested in the politicization of humans rights within the United Nations for some time. However, previous research typically looks at simple associations between voting coalitions and observable variables, such as geog...
A Dirichlet Process Mixture of Robust Task Models for Scalable Lifelong Reinforcement Learning [0.03%]
一种鲁棒任务模型的Dirichlet过程混合方法，用于可扩展的终身强化学习
Zhi Wang,Chunlin Chen,Daoyi Dong
Zhi Wang
While reinforcement learning (RL) algorithms are achieving state-of-the-art performance in various challenging tasks, they can easily encounter catastrophic forgetting or interference when faced with lifelong streaming information. In this ...
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