18.225 new books

From: Humanist Discussion Group (by way of Willard McCarty willard.mccarty_at_kcl.ac.uk>
Date: Sun, 19 Sep 2004 08:33:54 +0100

               Humanist Discussion Group, Vol. 18, No. 225.
       Centre for Computing in the Humanities, King's College London
                     Submit to: humanist_at_princeton.edu

         Date: Sun, 19 Sep 2004 08:30:30 +0100
         From: Willard McCarty <willard.mccarty_at_kcl.ac.uk>
         Subject: new books

Self and Substance in Leibniz


Marc Elliott Bobro
University of Southern Maine, Portland, ME, USA

We are omniscient but confused, says Leibniz. He also says that we live in
the best of all possible worlds, yet do not causally interact. So what are
we? Leibniz is known for many things, including the ideality of space and
time, calculus, plans for a universal language, theodicy, and ecumenism.
But he is not known for his ideas on the self and personal identity. This
book shows that Leibniz offers an original, internally coherent theory of
personal identity, a theory that stands on its own even next to Locke's
contemporaneous and more famous version. This book will appeal not only to
students of Leibniz's thought but also to philosophers and psychologists
interested in methodological problems in understanding or formulating
theories of self and personal identity.

Introduction. 1. Am I Essentially a Person? 2. What Makes Me a Person? 3.
What Makes Me the Same Person? 4. Could Thinking Machines be Moral Agents?
5. Why Bodies? 6. What Makes my Survival Meaningful? Conclusion. Appendix
A: On Hume. Appendix B: On Kant's Paralogisms. Bibliography. Index of
Proper Names.

Hard cover ISBN: 1-4020-2024-4 Date: June 2004 Pages: 151 pp.
EUR 67.00 / USD 74.00 / GBP 47.00

Geometric Data Analysis
  From Correspondence Analysis to Structured Data Analysis


Brigitte Le Roux
Universit? Ren? Descartes, Paris, France

Henry Rouanet
Universit? Ren? Descartes, Paris, France

"Geometric Data Analysis" (GDA) is the name suggested by P. Suppes
(Stanford University) to designate the approach to Multivariate Statistics
initiated by Benzecri as Correspondence Analysis, an approach that has
become more and more used and appreciated over the years. This book
presents the full formalization of GDA in terms of linear algebra -- the
most original and far-reaching consequential feature of the approach -- and
shows also how to integrate the standard statistical tools such as Analysis
of Variance, including Bayesian methods. Chapter 9, Research Case Studies,
is nearly a book in itself ; it presents the methodology in action on three
extensive applications, one for medicine, one from political science, and
one from education (data borrowed from the Stanford computer-based
Educational Program for Gifted Youth ). Thus the readership of the book
concerns both mathematicians interested in the applications of mathematics,
and researchers willing to master an exceptionally powerful approach of
statistical data analysis.

Foreword; Patrick Suppes. Preface. 1: Overview of Geometric Data Analysis.
1.1. CA of a Historical Data Set. 1.2. The Three Key Ideas of GDA. 1.3.
Three Paradigms of GDA. 1.4. Historical Sketch. 1.5. Methodological Strong
Points. 1.6. From Descriptive to Inductive Analysis. 1.7. Organization of
the Book. 2: Correspondence Analysis (CA). 2.1. Measure vs. Variable
Duality. 2.2. Measure over a Cartesian Product. 2.3. Correspondence
Analysis. 2.4. Extensions and Concluding Comments. Exercises. 3: Euclidean
Cloud. 3.1. Basic Statistics. 3.2. Projected Clouds. 3.3. Principle
Directions. 3.4. Principle Hyperellipsoids. 3.5. Between and within Clouds.
3.6. Euclidean Classification. 3.7. Matrix Formulas. 4: Principal Component
Analysis (PCA). 4.1. Biweighted PCA. 4.2. Simple PCA. 4.3. Standard PCA.
4.4. General PCA. 4.5. PCA of a Table of Measures. 4.6. Methodology of PCA.
5: Multiple Correspondence Analysis (MCA). 5.1. Standard MCA. 5.2. Specific
MCA. 5.3. Methodology of MCA. 5.4. The Culture Example. Exercises. 6:
Structured Data Analysis. 6.1. Structuring Factors. 6.2. Analysis of
Comparisons. 6.3. Additive and Interation Clouds. 6.4. Related Topics. 7:
Stability of a Euclidean Cloud. 7.1. Stability and Grouping. 7.2. Influence
of a Group of Points. 7.3. Change of Metric. 7.4. Influence of a Variable.
7.5. Basic Theorems. 8: Inductive Data Analysis. 8.1. Influence in
Multivariate Statistics. 8.2. Univariate Effects. 8.3. Combinatorial
Inference. 8.4. Bayesian Data Analysis. 8.5. Inductive GDA. 8.6. Guidelines
for Inductive Analysis. 9: Research Case Studies. 9.1. Parkinson Study.
9.2. French Political Space. 9.3. EPGY Study. 9.4. About Software. 10:
Mathematical Bases. 10.1. Matrix Operations. 10.2. Finite-dimensional
Vector Space. 10.3. Euclidean Vector Space. 10.4. Multidimensional
Geometry. 10.5. Spectral Theorem. Bibliography. Index. Name Index. Symbol
Index. Subject Index.

Hard cover ISBN: 1-4020-2235-2 Date: June 2004 Pages: 486 pp.
EUR 155.00 / USD 171.00 / GBP 107.00

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Dr Willard McCarty | Senior Lecturer | Centre for Computing in the
Humanities | King's College London | Strand | London WC2R 2LS || +44 (0)20
7848-2784 fax: -2980 || willard.mccarty_at_kcl.ac.uk
Received on Sun Sep 19 2004 - 03:58:13 EDT

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