Corrigendum to "Estimating bounds on causal effects in high-dimensional and possibly confounded systems" [Int. J. Approx. Reason. 88 (2017) 371-384] [0.03%]
“高维且可能具有混杂变量的系统中因果效应界限的估计”的勘误项
Daniel Malinsky,Peter Spirtes
Daniel Malinsky
[This corrects the article PMC5711475.].
Rosana Zanotelli,Renata Reiser,Benjamin Bedregal
Rosana Zanotelli
The n-dimensional fuzzy logic (n-DFL) has been contributed to overcome the insufficiency of traditional fuzzy logic in modeling imperfect and imprecise information, coming from different opinions of many experts by considering the possibili...
Jidapa Kraisangka,Marek J Druzdzel
Jidapa Kraisangka
Cox's proportional hazards (CPH) model is quite likely the most popular modeling technique in survival analysis. While the CPH model is able to represent a relationship between a collection of risks and their common effect, Bayesian network...
A Constraint Optimization Approach to Causal Discovery from Subsampled Time Series Data [0.03%]
子样本时间序列数据的因果发现的约束优化方法
Antti Hyttinen,Sergey Plis,Matti Järvisalo et al.
Antti Hyttinen et al.
We consider causal structure estimation from time series data in which measurements are obtained at a coarser timescale than the causal timescale of the underlying system. Previous work has shown that such subsampling can lead to significan...
Estimating bounds on causal effects in high-dimensional and possibly confounded systems [0.03%]
高维和可能受到混杂因素影响的系统中因果效应的估计上限研究
Daniel Malinsky,Peter Spirtes
Daniel Malinsky
We present an algorithm for estimating bounds on causal effects from observational data which combines graphical model search with simple linear regression. We assume that the underlying system can be represented by a linear structural equa...
Particle MCMC algorithms and architectures for accelerating inference in state-space models [0.03%]
粒子MCMC算法和加速状态空间模型推理的体系结构
Grigorios Mingas,Leonardo Bottolo,Christos-Savvas Bouganis
Grigorios Mingas
Particle Markov Chain Monte Carlo (pMCMC) is a stochastic algorithm designed to generate samples from a probability distribution, when the density of the distribution does not admit a closed form expression. pMCMC is most commonly used to s...
Modeling Women's Menstrual Cycles using PICI Gates in Bayesian Network [0.03%]
基于PICI_gate的贝叶斯网络建模女性月经周期
Adam Zagorecki,Anna Łupińska-Dubicka,Mark Voortman et al.
Adam Zagorecki et al.
A major difficulty in building Bayesian network (BN) models is the size of conditional probability tables, which grow exponentially in the number of parents. One way of dealing with this problem is through parametric conditional probability...
Michael Scott Balch
Michael Scott Balch
This paper introduces a new mathematical object: the confidence structure. A confidence structure represents inferential uncertainty in an unknown parameter by defining a belief function whose output is commensurate with Neyman-Pearson conf...
Jaime S Ide,Sheng Zhang,Chiang-Shan R Li
Jaime S Ide
Much effort has been made to better understand the complex integration of distinct parts of the human brain using functional magnetic resonance imaging (fMRI). Altered functional connectivity between brain regions is associated with many ne...
Machine learning-based receiver operating characteristic (ROC) curves for crisp and fuzzy classification of DNA microarrays in cancer research [0.03%]
基于机器学习的受试者操作特征(ROC)曲线在癌症研究中用于DNA微阵列的 crisp和fuzzy分类
Leif E Peterson,Matthew A Coleman
Leif E Peterson
Receiver operating characteristic (ROC) curves were generated to obtain classification area under the curve (AUC) as a function of feature standardization, fuzzification, and sample size from nine large sets of cancer-related DNA microarray...