Proceedings Of The Workshop On Causality And Causal Discovery
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Proceedings Of The Workshop On Causality And Causal Discovery
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Author : Kamran Karimi
language : en
Publisher: Regina : Department of Computer Science, University of Regina
Release Date : 2004-01-01
Proceedings Of The Workshop On Causality And Causal Discovery written by Kamran Karimi and has been published by Regina : Department of Computer Science, University of Regina this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-01-01 with categories.
Elements Of Causal Inference
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Author : Jonas Peters
language : en
Publisher: MIT Press
Release Date : 2017-11-29
Elements Of Causal Inference written by Jonas Peters and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-29 with Computers categories.
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.
Rethinking Causality In Quantum Mechanics
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Author : Christina Giarmatzi
language : en
Publisher: Springer Nature
Release Date : 2019-10-21
Rethinking Causality In Quantum Mechanics written by Christina Giarmatzi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-21 with Science categories.
Causality is central to understanding the mechanisms of nature: some event "A" is the cause of another event “B”. Surprisingly, causality does not follow this simple rule in quantum physics: due to to quantum superposition we might be led to believe that "A causes B” and that "B causes A”. This idea is not only important to the foundations of physics but also leads to practical advantages: a quantum circuit with such indefinite causality performs computationally better than one with definite causality. This thesis provides one of the first comprehensive introductions to quantum causality, and presents a number of advances. It provides an extension and generalization of a framework that enables us to study causality within quantum mechanics, thereby setting the stage for the rest of the work. This comprises: mathematical tools to define causality in terms of probabilities; computational tools to prove indefinite causality in an experiment; means to experimentally test particular causal structures; and finally an algorithm that detects the exact causal structure in an quantum experiment.
Bayesian Nets And Causality Philosophical And Computational Foundations
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Author : Jon Williamson
language : en
Publisher: Oxford University Press
Release Date : 2005
Bayesian Nets And Causality Philosophical And Computational Foundations written by Jon Williamson and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Computers categories.
Bayesian nets are used in artificial intelligence as a calculus for causal reasoning, enabling machines to make predictions perform diagnoses, take decisions and even to discover causal relationships. This book brings together how to automate reasoning in artificial intelligence, and the nature of causality and probability in philosophy.
Proceedings Of The 2020 Joint Workshop Of Fraunhofer Iosb And Institute For Anthropomatics Vision And Fusion Laboratory
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Author : Beyerer, Jürgen
language : en
Publisher: KIT Scientific Publishing
Release Date : 2021-06-22
Proceedings Of The 2020 Joint Workshop Of Fraunhofer Iosb And Institute For Anthropomatics Vision And Fusion Laboratory written by Beyerer, Jürgen and has been published by KIT Scientific Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-22 with Computers categories.
In 2020 fand der jährliche Workshop des Faunhofer IOSB und the Lehrstuhls für interaktive Echtzeitsysteme statt. Vom 27. bis zum 31. Juli trugen die Doktorranden der beiden Institute über den Stand ihrer Forschung vor in Themen wie KI, maschinellen Lernen, computer vision, usage control, Metrologie vor. Die Ergebnisse dieser Vorträge sind in diesem Band als technische Berichte gesammelt. - In 2020, the annual joint workshop of the Fraunhofer IOSB and the Vision and Fusion Laboratory of the KIT was hosted at the IOSB in Karlsruhe. For a week from the 27th to the 31st July the doctoral students of both institutions presented extensive reports on the status of their research and discussed topics ranging from computer vision and optical metrology to network security, usage control and machine learning. The results and ideas presented at the workshop are collected in this book.
Handbook Of Philosophical Logic
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Author : Dov M. Gabbay
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-08-28
Handbook Of Philosophical Logic written by Dov M. Gabbay and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-08-28 with Mathematics categories.
The fourteenth volume of the Second Edition covers central topics in philosophical logic that have been studied for thousands of years, since Aristotle: Inconsistency, Causality, Conditionals, and Quantifiers. These topics are central in many applications of logic in central disciplines and this book is indispensable to any advanced student or researcher using logic in these areas. The chapters are comprehensive and written by major figures in the field.
Cause Effect Pairs In Machine Learning
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Author : Isabelle Guyon
language : en
Publisher: Springer Nature
Release Date : 2019-10-22
Cause Effect Pairs In Machine Learning written by Isabelle Guyon and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-22 with Computers categories.
This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by machine learning algorithms. Based on the results of the ChaLearn Cause-Effect Pairs Challenge, this book reveals that the joint distribution of two variables can be scrutinized by machine learning algorithms to reveal the possible existence of a “causal mechanism”, in the sense that the values of one variable may have been generated from the values of the other. This book provides both tutorial material on the state-of-the-art on cause-effect pairs and exposes the reader to more advanced material, with a collection of selected papers. Supplemental material includes videos, slides, and code which can be found on the workshop website. Discovering causal relationships from observational data will become increasingly important in data science with the increasing amount of available data, as a means of detecting potential triggers in epidemiology, social sciences, economy, biology, medicine, and other sciences.
Practical Approaches To Causal Relationship Exploration
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Author : Jiuyong Li
language : en
Publisher: Springer
Release Date : 2015-03-25
Practical Approaches To Causal Relationship Exploration written by Jiuyong Li and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-03-25 with Computers categories.
This brief presents four practical methods to effectively explore causal relationships, which are often used for explanation, prediction and decision making in medicine, epidemiology, biology, economics, physics and social sciences. The first two methods apply conditional independence tests for causal discovery. The last two methods employ association rule mining for efficient causal hypothesis generation, and a partial association test and retrospective cohort study for validating the hypotheses. All four methods are innovative and effective in identifying potential causal relationships around a given target, and each has its own strength and weakness. For each method, a software tool is provided along with examples demonstrating its use. Practical Approaches to Causal Relationship Exploration is designed for researchers and practitioners working in the areas of artificial intelligence, machine learning, data mining, and biomedical research. The material also benefits advanced students interested in causal relationship discovery.
Exploratory Causal Analysis With Time Series Data
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Author : James M. McCracken
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Exploratory Causal Analysis With Time Series Data written by James M. McCracken and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments. Data analysis techniques are required for identifying causal information and relationships directly from such observational data. This need has led to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics. A practicing analyst can explore the literature to find many proposals for identifying drivers and causal connections in time series data sets. Exploratory causal analysis (ECA) provides a framework for exploring potential causal structures in time series data sets and is characterized by a myopic goal to determine which data series from a given set of series might be seen as the primary driver. In this work, ECA is used on several synthetic and empirical data sets, and it is found that all of the tested time series causality tools agree with each other (and intuitive notions of causality) for many simple systems but can provide conflicting causal inferences for more complicated systems. It is proposed that such disagreements between different time series causality tools during ECA might provide deeper insight into the data than could be found otherwise.
Proceedings Of The 2021 Joint Workshop Of Fraunhofer Iosb And Institute For Anthropomatics Vision And Fusion Laboratory
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Author : Beyerer, Jürgen
language : en
Publisher: KIT Scientific Publishing
Release Date : 2022-07-05
Proceedings Of The 2021 Joint Workshop Of Fraunhofer Iosb And Institute For Anthropomatics Vision And Fusion Laboratory written by Beyerer, Jürgen and has been published by KIT Scientific Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-05 with Computers categories.
2021, the annual joint workshop of the Fraunhofer IOSB and KIT IES was hosted at the IOSB in Karlsruhe. For a week from the 2nd to the 6th July the doctoral students extensive reports on the status of their research. The results and ideas presented at the workshop are collected in this book in the form of detailed technical reports.