A Multinomial Canonical Decomposition Model, with emphasis on the analysis of Multivariate Binary dataIn this paper, we propose to decompose the canonical parameter of a multinomial model into a set of participant scores and category scores. External information about the participants or the ...
{A}}\right)\). Two deep neural networks, termed encoders, learn modality-specific, batch-corrected multivariate normal distributions that represent the latent state of the cell based on the observed data,q(zR∣XR, S) andq(zA∣XA, S), from the expression and accessibility observations,...
摘要:Because it determines a center-outward ordering of observations in $\mathbb{R}^d$ with $d\geq 2$, the concept of statistical depth permits to define quantiles and ranks for multivariate data and use them for various statistical tasks (\textit{e.g.} inference, hypothesis testing). Where...
This may enhance the predictive ability of this approach in comparison with the analysis of a single panel because of the ‘multivariate advantage’7. The group of cells which are only predictive when in combination with other cell (sub)types, are thus considered essential constituents in the ...
Circular Binary Segmentation (CBS) is a permutation-based algorithm for array Comparative Genomic Hybridization (aCGH) data analysis. CBS accurately segments data by detecting change-points using a maximal-ttest; but extensive computational burden is involved for evaluating the significance of change-point...
In the next section, when describing the data, an explanation on how this might affect the results will be provided. 2.2. The clr and ilr transformations Despite the advantages of using the compositional data framework to analyze multivariate series, the sum of the vector x→ being equal to ...
Child malnutrition is a huge health problem having multifaceted consequences for child survival and long-term well-being. Although, several studies investigated stunting, underweight, and wasting in low- and middle-income countries, in Nigeria, the link
Transfer learning Machine learning Data science Unconventional Oil and gas 1. Introduction 1.1. Background During oil field development, the commonly used methods of analysis and decision making include data processing, model building, simulation, forecasting, history matching or calibration, and optimizati...
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We also use optional cookies for advertising, personalisation of content, usage analysis, and social media. By accepting optional cookies, you consent to the processing of your personal data - including transfers to third parties. Some third parties are outside of the European Economic Area, with...