☆ 4.6 Article

Causality Analysis with Information Geometry: A Comparison

ENTROPY (2023)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Psychology, Experimental

Causal inference in cognitive neuroscience

David Danks et al.

Summary: Causal inference is crucial in cognitive science and neuroscience, particularly cognitive neuroscience. While statistical knowledge is enough for prediction and diagnosis, causal knowledge is necessary for intervention and action. This paper focuses on the conceptual issues and assumptions of causal inference in cognitive neuroscience. It connects inference methods with goals and challenges and provides guidance on selecting appropriate tools for scientific tasks.

WILEY INTERDISCIPLINARY REVIEWS-COGNITIVE SCIENCE (2023)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Methods and tools for causal discovery and causal inference

Ana Rita Nogueira et al.

Summary: This article explores the complexity of causality and its significance in the field of artificial intelligence. Causality research aims at obtaining causal knowledge from observational data and estimating the impact of variable changes on outcomes. The article also provides a practical toolkit for researchers and practitioners, including software, datasets, and examples.

WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY (2022)

添加到收藏夹

Review Physics, Multidisciplinary

Information Geometry, Fluctuations, Non-Equilibrium Thermodynamics, and Geodesics in Complex Systems

Eun-jin Kim

Summary: Information theory provides an interdisciplinary method to understand important phenomena, with information geometric theory being particularly useful for quantifying the evolution of information over time. Recent developments in this theory focus on dynamic aspects of non-equilibrium processes, discussing the role of information rate, relative entropy, and information length in thermodynamic relations. The implications of geodesic solutions in information geometry for self-organization and control are also explored.

ENTROPY (2021)

添加到收藏夹

Article Neurosciences

Nonlinear System Identification of Neural Systems from Neurophysiological Signals

Fei He et al.

Summary: The human nervous system exhibits complex nonlinear behaviors that cannot be adequately captured by traditional linear analysis methods. Recent advances in nonlinear system identification have provided new insights into the understanding of neural functioning and potential biomarkers for neurological disorders. These approaches are crucial for the development of precision diagnostic tools.

NEUROSCIENCE (2021)

添加到收藏夹

Article Mathematics

The metric property of the quantum Jensen-Shannon divergence

Daniel Virosztek

Summary: The note proves that the square root of the quantum Jensen-Shannon divergence is a true metric on the cone of positive matrices, including the quantum state space.

ADVANCES IN MATHEMATICS (2021)

添加到收藏夹

Article Physics, Multidisciplinary

Causal Information Rate

Eun-jin Kim et al.

Summary: Information processing in complex systems is common and can be elucidated using information geometric theory. This paper introduces a new information-geometric measure of causality by calculating the effect of one variable on the information rate of the other variable, which proves to be a sensitive method for identifying causal relations.

ENTROPY (2021)

添加到收藏夹

Proceedings Paper Computer Science, Software Engineering

Histogram binning revisited with a focus on human perception

Raphael Sahann et al.

Summary: This study conducted a quantitative user research to evaluate how well users can visually perceive underlying data distribution from a histogram representation. Results show that more bins generally lead to fewer errors by viewers, but beyond a certain number of bins, adding more bins does not improve error rate. The study also found that existing mathematical models overestimate the number of bins required to make data distribution visible to human viewers.

2021 IEEE VISUALIZATION CONFERENCE - SHORT PAPERS (VIS 2021) (2021)

添加到收藏夹

Article Mechanics

Information geometry and non-equilibrium thermodynamic relations in the over-damped stochastic processes

Eun-jin Kim

Summary: Information geometry theory is an advantageous method for understanding complexity, allowing us to describe the characteristics of time-varying, non-equilibrium processes by measuring the change in information along the evolution path of a stochastic variable. By linking it with thermodynamic concepts, we can further explain the meaning of information length and information rate, as well as their relationship with entropy production and self-organization.

JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT (2021)

添加到收藏夹

Article Anesthesiology

Granger Causality of the Electroencephalogram Reveals Abrupt Global Loss of Cortical Information Flow during Propofol-induced Loss of Responsiveness

Rebecca M. Pullon et al.

ANESTHESIOLOGY (2020)

添加到收藏夹

Article Biology

Synchronous analysis of brain regions based on multi-scale permutation transfer entropy

Yunyuan Gao et al.

COMPUTERS IN BIOLOGY AND MEDICINE (2019)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Causal deconvolution by algorithmic generative models

Hector Zenil et al.

NATURE MACHINE INTELLIGENCE (2019)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Directed Connectivity Analysis of Functional Brain Networks during Cognitive Activity Using Transfer Entropy

Md. Hedayetul Islam Shovon et al.

NEURAL PROCESSING LETTERS (2017)

添加到收藏夹

Article Physics, Fluids & Plasmas

Low-algorithmic-complexity entropy-deceiving graphs

Hector Zenil et al.

PHYSICAL REVIEW E (2017)

添加到收藏夹

Article Physics, Multidisciplinary

A Recipe for the Estimation of Information Flow in a Dynamical System

Deniz Gencaga et al.

ENTROPY (2015)

添加到收藏夹

Article Physics, Fluids & Plasmas

Causality detection and turbulence in fusion plasmas

B. Ph van Milligen et al.

NUCLEAR FUSION (2014)

添加到收藏夹

Article Biochemical Research Methods

A new NARX-based Granger linear and nonlinear casual influence detection method with applications to EEG data

Yifan Zhao et al.

JOURNAL OF NEUROSCIENCE METHODS (2013)

添加到收藏夹

Article Ecology

Causality and Persistence in Ecological Systems: A Nonparametric Spectral Granger Causality Approach

Matteo Detto et al.

AMERICAN NATURALIST (2012)

添加到收藏夹

Article Meteorology & Atmospheric Sciences

Causal Discovery for Climate Research Using Graphical Models

Imme Ebert-Uphoff et al.

JOURNAL OF CLIMATE (2012)

添加到收藏夹

Article Neurosciences

Wiener-Granger Causality: A well established methodology

Steven L. Bressler et al.

NEUROIMAGE (2011)

添加到收藏夹

Article Physics, Multidisciplinary

Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables

Lionel Barnett et al.

PHYSICAL REVIEW LETTERS (2009)

添加到收藏夹

Article Optics

Properties of classical and quantum Jensen-Shannon divergence

Jop Briet et al.

PHYSICAL REVIEW A (2009)

添加到收藏夹

Article Neurosciences

Analyzing information flow in brain networks with nonparametric Granger causality

Mukeshwar Dhamala et al.

NEUROIMAGE (2008)

添加到收藏夹

Article Physics, Multidisciplinary

Estimating granger causality from fourier and wavelet transforms of time series data

Mukeshwar Dhamala et al.

PHYSICAL REVIEW LETTERS (2008)

添加到收藏夹

Article Economics

Nonparametric estimation of average treatment effects under exogeneity: A review

GW Imbens

REVIEW OF ECONOMICS AND STATISTICS (2004)

添加到收藏夹

Article Physics, Multidisciplinary

Measuring information transfer

T Schreiber

PHYSICAL REVIEW LETTERS (2000)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.