22 papers · ranked by Valyu relevance
Grigoriy Gogoshin, Sergio Branciamore, Andrei S. Rodin
Bayesian Network (BN) modeling is a prominent and increasingly popular computational systems biology method. It aims to construct network graphs from the large heterogeneous biological datasets that reflect the underlying biological relationships. Currently, a variety of strategies exist for evaluating BN methodology…
Alex E. Yuan, Wenying Shou
In disciplines from biology to climate science, a routine task is to compute a correlation between a pair of time series, and determine whether the correlation is statistically significant (i.e. unlikely under the null hypothesis that the time series are independent). This problem is challenging because time series…
Hongjian Shi, Mathias Drton, Han Fang
In recent work, Azadkia and Chatterjee (2021) laid out an ingenious approach to defining consistent measures of conditional dependence. Their fully nonparametric approach forms statistics based on ranks and nearest neighbor graphs. The appealing nonparametric consistency of the resulting conditional dependence measure…
Bilol Banerjee
This article deals with the problem of testing conditional independence between two random vectors X and Y given a confounding random vector Z. Several authors have considered this problem for multivariate data. However, most of the existing tests has poor performance against local contiguous alternatives beyond linear…
Alex Eric Yuan, Wenying Shou, Meredith C Schuman, Meredith C Schuman
Complex systems are challenging to understand, especially when they defy manipulative experiments for practical or ethical reasons. Several fields have developed parallel approaches to infer causal relations from observational time series. Yet, these methods are easy to misunderstand and often controversial. Here, we…
Lei Zan, Anouar Meynaoui, Charles K. Assaad, Emilie Devijver + 2 more
'Eric Gaussier' 'Antonio M. Scarfone'] In this study, we focus on mixed data which are either observations of univariate random variables which can be quantitative or qualitative, or observations of multivariate random variables such that each variable can include both quantitative and qualitative components. We first…
Suzanne H. Keddie, Oliver Baerenbold, Ruth H. Keogh, John Bradley
Background Latent class models are increasingly used to estimate the sensitivity and specificity of diagnostic tests in the absence of a gold standard, and are commonly fitted using Bayesian methods. These models allow us to account for ‘conditional dependence’ between two or more diagnostic tests, meaning that the…
Florian Griessenberger, Wolfgang Trutschnig, Robert R. Junker
Correlations belong to the standard repertoire of ecologists for quantifying the strength of dependence between two random variables. Classical dependence measures are usually not capable of detecting non-monotonic or non-functional dependencies. Furthermore, they completely fail to detect asymmetry and direction in…
Emilio Salinas, Terrence R Stanford
Intuitively, combining multiple sources of evidence should lead to more accurate decisions than considering single sources of evidence individually. In practice, however, the proper computation may be difficult, or may require additional data that are inaccessible. Here, based on the concept of conditional…
Hrishikesh D. Vinod
Measuring the (causal) direction and strength of dependence between two variables (events), Xi and Xj , is fundamental for all science. Our survey of decades-long literature on statistical dependence reveals that most assume symmetry in the sense that the strength of dependence of Xi on Xj exactly equals the strength…
Charles K. Assaad, Emilie Devijver, Eric Gaussier, Philip Broadbridge + 2 more
'Philip Broadbridge' 'Fan Yang' 'Wenkai Hu'] This study addresses the problem of learning a summary causal graph on time series with potentially different sampling rates. To do so, we first propose a new causal temporal mutual information measure for time series. We then show how this measure relates to an entropy…
Emilio Salinas, Terrence R. Stanford, Nicholas V Swindale
Intuitively, combining multiple sources of evidence should lead to more accurate decisions than considering single sources of evidence individually. In practice, however, the proper computation may be difficult, or may require additional data that are inaccessible. Here, based on the concept of conditional…
Lu Lu, Sujit K. Ghosh
Conditional copulas are useful tools for modeling the dependence between multiple response variables that may vary with a given set of predictor variables. Conditional dependence measures such as conditional Kendall's tau and Spearman's rho that can be expressed as functionals of the conditional copula are often used…
Jinyuan Chang, Yue Du, Jing He, Qiwei Yao
We propose new statistical tests, in high-dimensional settings, for testing the independence of two random vectors and their conditional independence given a third random vector. The key idea is simple, i.e., we first transform each component variable to standard normal via its marginal empirical distribution, and we…
Dawid Tarłowski
It is well known that the independent random variables X and Y are uncorrelated in the sense E[XY] = E[X] ·E[Y] and that the implication may be reversed in very specific cases only. This paper proves that under general assumptions the conditional uncorrelation of random variables, where the conditioning takes place…
Vladimir Vovk
A very simple example demonstrates that Fisher's application of the conditionality principle to regression ("fixed x regression"), endorsed by Sprott and many other followers, makes prediction impossible in the context of statistical learning theory. On the other hand, relaxing the requirement of conditionality makes…
Joseph A. Bulbulia
In a causal diagram, a path is ‘blocked’ or ‘d-separated’ if a node along it interrupts causation. Two variables are d-separated if all paths connecting them are blocked, making them conditionally independent. Conversely, unblocked paths result in ‘d-connected’ variables, implying potential dependence (Pearl, , ).…
Alex E. Yuan, Wenying Shou
In disciplines from ecology to neuroscience, researchers analyze correlations between pairs of nonstationary time series to infer relationships among variables. This often involves a statistical test to determine whether an observed correlation is stronger than expected under the null hypothesis of independence.…
Alex E. Yuan, Wenying Shou
Researchers frequently analyze correlations between pairs of time series by determining whether an observed correlation is stronger than expected under the null hypothesis of independence. However, the time series are often nonstationary, with statistical properties that change over time, thereby making standard tests…
Authors not listed
Solving optimization problems, especially for nonlinear and constrained systems, is a challenge. Decades of specialized algorithms have been developed for general and special cases of root finding, minimization (including constraints), for parameter estimation, and mapping connected spaces. These approaches typically…
Sara Giarrusso, Paola Gori-Giorgi, Federica Agostini
We generalize the definitions of local scalar potentials named vkin and vN−1, which are relevant to properly describe phenomena such as molecular dissociation with density-functional theory, to the case in which the electronic wavefunction corresponds to a complex current-carrying state. In such a case, an extra term…
Authors not listed
Two kinetic schemes of the general modifier mechanism have been analysed in a quasi-steady state approximation, assuming that the reaction product concentration is negligible (a natural assumption for the initial rate method) and without additional simplifying assumptions. The characteristic equations have been…