Multiple datasets, joint and marginal expectations
Joint Analysis of Multiple Datasets by Cross-Cumulant Tensor (Block) Diagonalization
... two datasets, due to noise and finite sample ...ent datasets can achieve enhanced resilience to ...two datasets in a single tensor factorization, as opposed to ...
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jMOSAiCS: joint analysis of multiple ChIP seq datasets
... The key components of jMOSAiCS are base models for the sequencing reads of each individual ChIP-seq experi- ment and a model that governs the relationship of enrich- ment among different samples. We chose well-developed ...
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Simultaneous comparisons of treatments at multiple time points: combined marginal models versus joint modeling
... This situation rather demands many-to-one tests applied to each of several points in time, which naturally acknowledges the multiplicity of treatment groups but leaves open how multiplicity of occasions should be ...
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Pseudo-marginal algorithms with multiple CPUs
... the marginal space of the model parameter (with latent variables integrated out), with a target distribution that is still the correct posterior ...the marginal space relative to the joint space of ...
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Marginal and joint reliability importance based on survival signature.
... and joint reliability importance measures have been found to be useful in optimal system ...of marginal and joint reliability importance for a coherent system that consists of multiple types ...
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Joint Analysis for Multiple Traits
... a marginal regression model, allow the variant having different effect sizes and effect directions on different ...and multiple traits as independent predictors, therefore, reverse regression models do not ...
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Joint Inference in Weakly-Annotated Image Datasets via Dense Correspondence
... Received: 6 July 2013 / Accepted: 15 February 2016 / Published online: 21 March 2016 © The Author(s) 2016. This article is published with open access at Springerlink.com Abstract We present a principled framework for ...
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Joint modelling of multiple network wiews
... space joint model (LSJM) that merges the information given by multiple network views assuming that the probability of a node being connected with other nodes in each network view is explained by a unique ...
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Joint modelling of multiple network wiews
... space joint model (LSJM) that merges the information given by multiple network views assuming that the probability of a node being connected with other nodes in each network view is explained by a unique ...
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Learning 3D joint constraints from vision-based motion capture datasets
... A balanced (50%/50%) subset is selected from these sampled valid/invalid instances using random sampling. The subset is again divided into training, validation, and test samples in a ratio (70%, 15%, 15%). For com- ...
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Joint Generation of Transliterations from Multiple Representations
... the datasets, we can no longer apply the phonetic approach, but we can still compare the orthographic approach to our joint approach, which can handle the lack of a phonetic transcription in some of the ...
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Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions
... our multiple co-clustering method only with co-clustering and restricted multiple clustering methods, which we model to accommodate different types of ...100 datasets for each setting, which resulted ...
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Using Graphics Hardware for Multiple Datasets Visualization
... of multiple sets of volumetric scalar ...for multiple datasets visualization (MDV) compared to the software-based ...the datasets being ...5 datasets, then the second regime with medium ...
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Bayesian correlated clustering to integrate multiple datasets
... 2.2 Dependent component allocations We are interested in the situation where we have a collection of n genes, for each of which we have measurements from K different data sources. One possible modelling approach would be ...
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COMPREHENSIVE EVALUATION AND INTERPRETATION OF PCA ON MULTIPLE DATASETS
... 6 CONCLUSION Dimension reduction was brought in to overcome the problem of dimensionality when dealing with high dimensional data. Principal component analysis is used as a tool for reducing the number of variables in ...
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Joint and Marginal Diagnostic Tests for Conditional Mean and Variance Specifications
... correct joint speci…cation of the conditional mean and variance 1 ...new joint and marginal speci…cation tests especially convenient for …nancial and economic ...of joint and marginal ...
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Consistent Expectations, Rational Expectations, Multiple-Solution Indeterminacies, and Least-Squares Learnability
... 7 This procedure was given the name “minimum state variable” (MSV) solution by Evans (1986), who referred to the step of choosing between the two roots in the last example as constituting a “subsidiary principle.” In ...
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Econophysics of Asset Price, Return and Multiple Expectations
... and multiple kinds of ...of expectations can cause fluctuations of trade volume, price and ...of expectations as weighted sum of partial price and trade volume disturbances for transactions made ...
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Distributed Data Mining for Multiple Sourced Heterogeneous Datasets: A Survey
... The Problem of Distributed Data Mining for Multiple Sourced Heterogeneous Data Sets A Problem Scenario With the development of all walks of life, the collection of data has become a problem. The systems of various ...
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CoINcIDE: A framework for discovery of patient subtypes across multiple datasets
... CoINcIDE, but their prognostic significance across multiple datasets provides a robust platform from which to validate them further. More importantly, the TCGA ovarian publication stated that the reported ...
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