'Causal structure' presentation slideshows

Causes and coincidences

Causes and coincidences. Tom Griffiths Cognitive and Linguistic Sciences Brown University.

981 views • 80 slides

Causal Cognition 1: learning

1.06k views • 70 slides

Review: Which of the following two statements is true?

Review: Which of the following two statements is true?. 1. P(Fido is an animal/ Fido is a dog) = 100%. 2. P(Fido is a dog/ Fido is an animal) = 100%. Experiments. Scientific Method. Last time we discussed the hypothetico -deductive method, which consisted of four steps:

626 views • 62 slides

Causality challenge #2: Pot-Luck

661 views • 54 slides

Lifecourse Epidemiology: Relevance for Studying Health Disparities in Cognitive Aging

Lifecourse Epidemiology: Relevance for Studying Health Disparities in Cognitive Aging. Maria Glymour Friday Harbor Psychometrics Workshop June 12, 2012. Acknowledgements. Funded in part by Grant R13AG030995-01A1 from the National Institute on Aging

692 views • 53 slides

Overview

An Introduction to Causal Modeling and Discovery Using Graphical Models Greg Cooper University of Pittsburgh. Overview. Introduction Representation Inference Learning Evaluation. What Is Causality?. Much consideration in philosophy I will treat it as a primitive

575 views • 48 slides

Subbarao Kambhampati Arizona State University

IPAM Machine Reasoning Workshops 3&4. Audio and pptx versions available at http://rakaposhi.eas.asu.edu/tmp/onr-ipam. Incomplete Domain Models, Uncertain Users, Unending Planning & Open Worlds Model-Lite Planning for Autonomy in the presence of Humans. Subbarao Kambhampati

577 views • 47 slides

Learning Causality

701 views • 38 slides

Causal reasoning in Biomedical Informatics

Causal reasoning in Biomedical Informatics. Chitta Baral Professor Department of Computer Science and Engineering chitta@asu.edu. Causal connection versus Correlation. Rain, Falling Barometer: They usually have a 1-1 correspondence Does falling barometer cause rain?

497 views • 37 slides

Regression: (2) Multiple Linear Regression and Path Analysis

Regression: (2) Multiple Linear Regression and Path Analysis. Hal Whitehead BIOL4062/5062. Multiple Linear Regression and Path Analysis. Multiple linear regression assumptions parameter estimation hypothesis tests selecting independent variables collinearity polynomial regression

1.15k views • 32 slides

Causal Modeling for Anomaly Detection

Causal Modeling for Anomaly Detection. Andrew Arnold Machine Learning Department, Carnegie Mellon University Summer Project with Naoki Abe Predictive Modeling Group, IBM Rick Lawrence, Manager June 23, 2006. Contributions.

493 views • 32 slides

Free energy and active inference (from attractors to action understanding) Karl Friston, University College London

Free energy and active inference (from attractors to action understanding) Karl Friston, University College London.

563 views • 30 slides

King Fahd University of Petroleum & Minerals

King Fahd University of Petroleum & Minerals. CEM - 515 Quality Assurance. Term Paper Presentation. Understanding and Measuring the Cost of Quality on Construction Projects . By Jihad Farhat. Table of Contents. Objective Construction Quality cost Approaches Quality Costs

494 views • 22 slides

Causal learning in humans

Causal learning in humans . Alison Gopnik Dept. of Psychology UC-Berkeley. East Coast Solutions Structured Abstract Innate Domain-specific Modularity. West Coast Solutions Distributed Concrete Learned Domain-General Connectionism. Knowledge as an inverse problem. Structured

449 views • 20 slides

Black hole history (I)

Black hole history (I). 1783 : The Reverend John Michell (invisible sphere) 1798,1799 : P.S. Laplace (Exposition du Systeme du Monde Part II, p. 305, Allgemeine Geographifche Ephemeriden, 4, S.1, 1799) 1915 : K. Schwarzschild

304 views • 20 slides

Bert Leuridan 14 June 2006 Bert.Leuridan@UGent.be

Galton's blinding glasses Modern statistics hiding causal structure in early theories of inheritance. Centre for Logic and Philosophy of Science Ghent University Belgium. Bert Leuridan 14 June 2006 Bert.Leuridan@UGent.be. 1. The Problem. The Problem. Gregor Mendel (1865-1866)

404 views • 20 slides

Causal learning in humans

Causal learning in humans. Alison Gopnik Dept. of Psychology UC-Berkeley. East Coast Solutions Structured Abstract Innate Domain-specific Modularity. West Coast Solutions Distributed Concrete Learned Domain-General Connectionism. Knowledge as an inverse problem. Structured

280 views • 20 slides

A Non-Parametric Bayesian Method for Inferring Hidden Causes

A Non-Parametric Bayesian Method for Inferring Hidden Causes. by F. Wood, T. L. Griffiths and Z. Ghahramani. Discussion led by Qi An ECE, Duke University. Outline. Introduction A generative model with hidden causes Inference algorithms Experimental results Conclusions. Introduction.

230 views • 17 slides

Jaakko Kuorikoski Jaakko.kuorikoski@helsinki.fi Trends and tensions in intellectual integration

Mechanisms, Modularity and Constitutive Explanation Mechanisms and Causality in the Sciences 10.9.2009 University of Kent, Canterbury, UK. Jaakko Kuorikoski Jaakko.kuorikoski@helsinki.fi Trends and tensions in intellectual integration www.helsinki.fi/filosofia/tint /

301 views • 15 slides

Mediating Between Causes and Probabilities: the Use of Graphical Models in Econometrics

221 views • 14 slides