Clark Glymour

Last updated
Conference on the Foundations of Space-Time Theories (1977). Earman, John; Glymour, Clark N; Stachel, John J (eds.). Foundations of space-time theories. Minneapolis: University of Minnesota Press. ISBN   978-0-8166-5752-0. OCLC   783679604.
  • Theory and Evidence (Princeton, 1980)
  • Examining Holistic Medicine (with D. Stalker), Prometheus, 1985
  • Foundations of Space-Time Theories (with J. Earman), University of Minnesota Press, 1986
  • Discovering Causal Structure (with R. Scheines, P. Spirtes and K.Kelly) Academic Press, 1987
  • Glymour, Clark N; Stalker, Douglas Frank, eds. (1989). Examining holistic medicine. Buffalo, N.Y.: Prometheus Books. ISBN   978-0-87975-553-9. OCLC   978008570.
  • Causation, Prediction and Search (with P.Spirtes and R. Scheines), Springer, 1993, 2nd Edition MIT Press, 2001
  • Thinking Things Through, MIT Press, 1994
  • Android Epistemology (with K. Ford and P. Hayes) MIT/AAAI Press, 1996
  • The Mind's Arrows: Bayes Nets and Graphical Causal Models in Psychology, MIT Press, 2001
  • Earman, John; Glymour, Clark N; Mitchell, Sandra D, eds. (2003). Ceteris paribus laws. Dordrecht; Boston: Kluwer Academic Publishers. ISBN   978-1-4020-1020-0. OCLC   949277608.
  • Galileo in Pittsburgh Harvard University Press, 2010.
  • Journal articles

    • "The Evaluation of Discovery: Models, Simulation and Search through “Big Data”", Open Philosophy, 2019. Available on-line (Open Access): https://doi.org/10.1515/opphil-2019-0005
    • "When is a Brain Like the Planet?", Philosophy of Science , 2008.
    • (with David Danks) "Reasons as Causes in Bayesian Epistemology", Journal of Philosophy , 2008.
    • "Markov Properties and Quantum Experiments", in W. Demopoulos and I. Pitowsky, eds. Physical Theory and Its Interpretation: Essays in Honor of Jeffrey Bub , Springer 2006.
    • (with Chu, T. and David Danks) "Data Driven Methods for Granger Causality and Contemporaneous Causality with Non-Linear Corrections: Climate Teleconnection Mechanisms", 2004.
    • "Review of Phil Dowe and Paul Nordhoff: Cause and Chance: Causation in an Indeterministic World", Mind , 2005.
    • (with Eberhardt, Frederick, and Richard Scheines). "N-1 Experiments Suffice to Determine the Causal Relations Among N Variables", 2004.
    • (with F. Eberhardt and R. Scheines), "Log2(N) Experiments are Sufficient, and in the Worst Case Necessary, for Identifying Causal Structure", UAI Proceedings, 2005
    • (with Handley, Daniel, Nicoleta Serban, David Peters, Robert O'Doherty, Melvin Field, Larry Wasserman, Peter Spirtes, and Richard Scheines), "Evidence of systematic expressed sequence tag IMAGE clone cross-hybridization on cDNA microarrays", Genomics , Volume 83, Issue 6 (June, 2004), pages 1169–1175.
    • (with Handley, Daniel, Nicoleta Serban, and David G. Peters). "Concerns About Unreliable Data from Spotted cDNA Microarrays Due to Cross-Hybridization and Sequence Errors", Statistical Applications in Genetics and Molecular Biology , Volume 3, Issue 1 (October 6, 2004), Article 25.
    • "Comment on D. Lerner", "The Illusion of Conscious Will", Behavioral and Brain Sciences, in press.
    • "Review of Joseph E. Early, Sr. (Ed.): Chemical Explanation: Characteristics, Development, Autonomy", Philosophy of Science, Volume 71, Number 3 (July, 2004), pages 415–418.
    • (with Spirtes, and Peter Glymour). "Causal Inference", Encyclopedia of Social Science , in press
    • "We believe in freedom of the will so that we can learn", Behavioral and Brain Sciences , Volume 27, Number 5 (2004), pages 661–662.
    • "The Automation of Discovery", Daedelus , Volume Winter (2004), pages 69–77.
    • (with Serban, Nicoleta, Larry Wasserman, David Peters, Peter Spirtes, Robert O'Doherty, Dan Handley, and Richard Scheines). "Analysis of microarray data for treated fat cells", (2003).
    • (with Danks, David, and Peter Spirtes). "The Computational and Experimental Complexity of Gene Perturbations for Regulatory Network Search", (2003).
    • (with Silva, Ricardo, Richard Scheines, and Peter Spirtes). "Learning Measurement Models for Unobserved Variables", UAI '03, Proceedings of the 19th Conference in Uncertainty in Artificial Intelligence, August 7–10, 2003, Acapulco, Mexico (2003), pages 543–550.
    • (with Danks, David and Peter Spirtes). "The Computational and Experimental Complexity of Gene Perturbations for Regulatory Network Search", Proceedings of IJCAI-2003 Workshop on Learning Graphical Models for Computational Genomics, (2003), pages 22–31.
    • (with Frank Wimberly, Thomas Heiman, and Joseph Ramsey). "Experiments on the Accuracy of Algorithms for Inferring the Structure of Genetic Regulatory Networks from Microarray Expression Levels", International Joint Conference on Artificial Intelligence Workshop, 2003
    • "A Semantics and Methodology for Ceteris Paribus Hypotheses", Erkenntnis , Volume 57 (2002), pages 395–405.
    • "Review of James Woodward, Making Things Happen: A Theory of Causal Explanation", British Journal for Philosophy of Science , Volume 55 (2004), pages 779–790.
    • (with Fienberg, Stephen, and Richard Scheines). "Expert statistical testimony and epidemiological evidence: the toxic effects of lead exposure on children", Journal of Econometrics , Volume 113 (2003), pages 33–48.
    • "Learning, prediction and causal Bayes Nets", Trends in Cognitive Sciences , Volume 7, Number 1 (2003), pages 43–47.
    • (with Alison Gopnik, David M. Sobel, Laura E. Schulz, Tamar Kushnir, and David Danks). "A theory of causal learning in children: Causal maps and Bayes nets", Psychological Review , Volume 111, Number 1 (2004).
    • "Freud, Kepler, and the clinical evidence", in R. Wollheim and J. Hopkins, eds. Philosophical Essays on Freud , Cambridge University Press 1982.
    • and many others dating back to 1970.

    Related Research Articles

    Bayesian probability is an interpretation of the concept of probability, in which, instead of frequency or propensity of some phenomenon, probability is interpreted as reasonable expectation representing a state of knowledge or as quantification of a personal belief.

    <span class="mw-page-title-main">Epistemology</span> Branch of philosophy concerning knowledge

    Epistemology is the branch of philosophy concerned with knowledge. Epistemologists study the nature, origin, and scope of knowledge, epistemic justification, the rationality of belief, and various related issues. Debates in contemporary epistemology are generally clustered around four core areas:

    Social epistemology refers to a broad set of approaches that can be taken in epistemology that construes human knowledge as a collective achievement. Another way of characterizing social epistemology is as the evaluation of the social dimensions of knowledge or information.

    Causality is an influence by which one event, process, state, or object (acause) contributes to the production of another event, process, state, or object (an effect) where the cause is partly responsible for the effect, and the effect is partly dependent on the cause. In general, a process has many causes, which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future. Some writers have held that causality is metaphysically prior to notions of time and space.

    Scientific evidence is evidence that serves to either support or counter a scientific theory or hypothesis, although scientists also use evidence in other ways, such as when applying theories to practical problems. Such evidence is expected to be empirical evidence and interpretable in accordance with the scientific method. Standards for scientific evidence vary according to the field of inquiry, but the strength of scientific evidence is generally based on the results of statistical analysis and the strength of scientific controls.

    A Bayesian network is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). While it is one of several forms of causal notation, causal networks are special cases of Bayesian networks. Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases.

    Understanding is a cognitive process related to an abstract or physical object, such as a person, situation, or message whereby one is able to use concepts to model that object. Understanding is a relation between the knower and an object of understanding. Understanding implies abilities and dispositions with respect to an object of knowledge that are sufficient to support intelligent behavior.

    Minimum Description Length (MDL) is a model selection principle where the shortest description of the data is the best model. MDL methods learn through a data compression perspective and are sometimes described as mathematical applications of Occam's razor. The MDL principle can be extended to other forms of inductive inference and learning, for example to estimation and sequential prediction, without explicitly identifying a single model of the data.

    A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning.

    <span class="mw-page-title-main">Trygve Haavelmo</span> Norwegian economist and econometrician

    Trygve Magnus Haavelmo, born in Skedsmo, Norway, was an economist whose research interests centered on econometrics. He received the Nobel Memorial Prize in Economic Sciences in 1989.

    Computational epistemology is a subdiscipline of formal epistemology that studies the intrinsic complexity of inductive problems for ideal and computationally bounded agents. In short, computational epistemology is to induction what recursion theory is to deduction. It has been applied to problems in philosophy of science.

    Formal epistemology uses formal methods from decision theory, logic, probability theory and computability theory to model and reason about issues of epistemological interest. Work in this area spans several academic fields, including philosophy, computer science, economics, and statistics. The focus of formal epistemology has tended to differ somewhat from that of traditional epistemology, with topics like uncertainty, induction, and belief revision garnering more attention than the analysis of knowledge, skepticism, and issues with justification.

    Android epistemology is an approach to epistemology considering the space of possible machines and their capacities for knowledge, beliefs, attitudes, desires and for action in accord with their mental states. Thus, android epistemology incorporates artificial intelligence, computational cognitive psychology, computability theory and other related disciplines.

    Sandra D. Mitchell is an American philosopher of science and historian of ideas. She holds the position of distinguished professor in the department of History and Philosophy of Science at the University of Pittsburgh, the top rated school in the world for the subject according to the 2011 Philosophical Gourmet Report. Her research focuses on the philosophy of biology and the philosophy of social science, and connections between the two.

    <span class="mw-page-title-main">Alison Gopnik</span> American psychologist (born 1955)

    Alison Gopnik is an American professor of psychology and affiliate professor of philosophy at the University of California, Berkeley. She is known for her work in the areas of cognitive and language development, specializing in the effect of language on thought, the development of a theory of mind, and causal learning. Her writing on psychology and cognitive science has appeared in Science, Scientific American, The Times Literary Supplement, The New York Review of Books, The New York Times, New Scientist, Slate and others. Her body of work also includes four books and over 100 journal articles.

    Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. Typically it involves establishing four elements: correlation, sequence in time, a plausible physical or information-theoretical mechanism for an observed effect to follow from a possible cause, and eliminating the possibility of common and alternative ("special") causes. Such analysis usually involves one or more artificial or natural experiments.

    <span class="mw-page-title-main">Richard Neapolitan</span>

    Richard Eugene Neapolitan was an American scientist. Neapolitan is most well-known for his role in establishing the use of probability theory in artificial intelligence and in the development of the field Bayesian networks.

    Causal analysis is the field of experimental design and statistical analysis pertaining to establishing cause and effect. Exploratory causal analysis (ECA), also known as data causality or causal discovery is the use of statistical algorithms to infer associations in observed data sets that are potentially causal under strict assumptions. ECA is a type of causal inference distinct from causal modeling and treatment effects in randomized controlled trials. It is exploratory research usually preceding more formal causal research in the same way exploratory data analysis often precedes statistical hypothesis testing in data analysis

    Fredrick Eberhardt is an American philosopher and professor of philosophy at the California Institute of Technology. Previously he was a faculty member in the Philosophy-Neuroscience-Psychology program at Washington University in St. Louis. Eberhardt is known for his works on philosophy of science.

    References

    1. "Clark Glymour". Carnegie Mellon University. Retrieved December 16, 2019.
    2. Discovering Causal Structure. 1987. doi:10.1016/c2013-0-10734-9. ISBN   9780122869617.
    3. "Awards and Elections, Fall 2019". Center for Advanced Study in Behavioral Sciences. Retrieved December 16, 2019.
    4. "Romanell-Phi Beta Kappa Professorship Past Winners". Phi Beta Kappa. Retrieved December 16, 2019.
    5. "Clark Glymour". American Academy of Arts and Sciences. Retrieved December 16, 2019.
    6. P. Spirtes, C. Glymour, R. Scheines, Causation, Prediction and Search, Springer Lecture Notes in Statistics, 1993.
    7. Epistemology: 5 Questions Edited by Vincent F. Hendricks and Duncan Pritchard, September 2008, ISBN   87-92130-07-0.
    8. "Clark Glymour" . Retrieved December 16, 2019.
    9. "Bayesian Epistemology". July 12, 2001.
    10. Glymour, C.; Theory and evidence (1981), pages 63-93.
    11. Publications TETRAD. Retrieved December 16, 2019.
    12. Glymour, Clark; Scheines, Richard; Spirtes, Peter; Kelly, Kevin. "TETRAD: Discovering Causal Structure" Multivariate Behavioral Research 23.2 (1988). 10 July 2010. doi : 10.1207/s15327906mbr2302_13. PMID   26764954.
    Clark Glymour
    Born1942
    Academic background
    Alma mater University of New Mexico
    Indiana University Bloomington (Ph.D., 1969)