"This book is a delight." -- Barak
Pearlmutter, University of New Mexico
"This delightful book illustrates beautifully
the paradigm shift in physics from writing
equations and
solving them to computer modeling and experimentation."
-- Greg Chaitin, author of The Limits of
Mathematics
"Simulation," writes Gary Flake
in his preface, "becomes a form of experimentation
in a
universe of theories. The primary purpose
of this book is to celebrate this fact."
In this book, Gary William Flake develops
in depth the simple idea that recurrent rules
can
produce rich and complicated behaviors. Distinguishing
"agents" (e.g., molecules, cells,
animals, and species) from their interactions
(e.g., chemical reactions, immune system
responses, sexual reproduction, and evolution),
Flake argues that it is the computational
properties of interactions that account for
much of what we think of as "beautiful"
and
"interesting." From this basic
thesis, Flake explores what he considers
to be today's four
most interesting computational topics: fractals,
chaos, complex systems, and adaptation.
Each of the book's parts can be read independently,
enabling even the casual reader
to understand and work with the basic equations
and programs. Yet the parts are
bound together by the theme of the computer
as a laboratory and a metaphor for
understanding the universe. The inspired
reader will experiment further with the ideas
presented
to create fractal landscapes, chaotic systems,
artificial life forms, genetic algorithms,
and
artificial neural networks.
1998
ISBN 0-262-06200-3
492 pp., 173 illus. (cloth)
Computer science and artificial intelligence
are increasingly used in the hazardous and
uncertain realms of medical decision making,
where small faults or errors can spell human
catastrophe. This book describes, from both
practical and theoretical perspectives, an
AI
technology for supporting sound clinical
decision making and safe patient management.
The
technology combines techniques from conventional
software engineering with a
systematic method for building intelligent
agents. Although the focus is on medicine,
many
of the ideas can be applied to AI systems
in other hazardous settings. The book also
covers
a number of general AI problems, including
knowledge representation and expertise
modeling, reasoning and decision making under
uncertainty, planning and scheduling, and
the
design and implementation of intelligent
agents.
The book, written in an informal style, begins
with the medical background and motivations,
technical challenges, and proposed solutions.
It then turns to a wide-ranging discussion
of
intelligent and autonomous agents, with particular
reference to safety and hazard
management. The final section provides a
detailed discussion of the knowledge
representation and other aspects of the agent
model developed in the book, along with a
formal logical semantics for the language.
April 2000
ISBN 0-262-06211-9
300 pp. (cloth)
Parallel computation will become the norm
in the coming decades. Unfortunately, advances
in
parallel hardware have far outpaced parallel
applications of software. There are currently
two approaches to applying parallelism to
applications. One is to write completely
new
applications in new languages. But abandoning
applications that work is unacceptable to
most
nonacademic users of high-performance computers.
The other approach is to convert
existing applications to a parallel form.
This can be done manually or automatically.
Even partial
success in doing the job automatically has
obvious economic advantages.
This book describes a fundamentally new theoretical
framework for finding poor
algorithms in an application program and
replacing them with ones that parallelize
the code.
May 2000
ISBN 0-262-13368-7
233 pp. (cloth)
This study of learning in autonomous agents
offers a bracing intellectual adventure.
Chris
Thornton makes the compelling claim that
learning is not a passive discovery operation
but an active process involving creativity
on the part of the learner. Although theorists
of
machine learning tell us that all learning
methods contribute some form of bias and
thus
involve a degree of creativity, Thornton
carries the idea much further. He describes
an
incremental process, recursive relational
learning, in which the results of one learning
step serve as the basis for the next. Very
high-level recodings are then substantially
the
creative artifacts of the learner's own processing.
Lower-level recodings are more
"objective" in that their properties
are more severely constrained by the source
data.
Thornton sees consciousness as a process
at the outer fringe of relational learning,
just prior
to the onset of creativity. According to
this view, we cannot assume consciousness
to be an
exclusively human phenomenon, but rather
the expected feature of any cognitive mechanism
able to engage in extended flights of relational
learning.
Thornton presents key background material
in an entertaining manner, using extensive
mental
imagery and a minimum of mathematics. Anecdotes
and dialogue add to the text's informality.
March 2000
ISBN 0-262-20127-5
224 pp., 48 illus. (cloth)
Description
Readership: Econometrics and statistics postgraduates.
Professors and researchers in economics departments,
business schools, statistics departments,
or any research centre in the same fields,
especially econometricians.
This book offers an up-to-date coverage of
the basic principles and of the tools of
Bayesian inference in econometrics. Bayesian
inference is a branch of statistics that
integrates explicitly both data and prior
(possibly subjective) information in model
building , estimation and evaluation.
The book then shows how to use Bayesian methods
in a range of models especially suited to
the analysis of macroeconomic and financial
time series.
Contents/contributors
Chapter 1: Decision Theory and Bayesian Inference
Chapter 2: Bayesian Statistics and Linear
Regression
Chapter 3: Methods of Numerical Integration
Chapter 4: Prior Densities for the Regression
Model
Chapter 5: Dynamic Regression Models
Chapter 6: Bayesian Unit Roots
Chapter 7: Heteroskedasticity and ARCH
Chapter 8: Nonlinear Tome Series Models
Chapter 9: Systems of Equations
Appendix A: Probability Distributions
Appendix B: Generating Random Numbers
Hardback, 0-19-877312-9
Publication date: January 2000
Paperback, 0-19-877313-7
376 pages, 234mm x 156mm
An accessible guide to understanding probability
Uses a range of real life examples, such
as the lottery, and
horseracing Reveals many common fallacies
Written by an expert in the field of mathematics
Description
Readership: General readership, gambling
enthusiasts, card players
What are the odds against winning the Lottery,
making money in a casino, or backing the
right horse. Every day, people make judgements
on these matters and face other decisions
that rest on their understanding of
probability: buying insurance, following
medical advice, carrying an umbrella. Yet
many of us have a frightening ignorance of
how probability works.
Taking Chances presents an entertaining and
fascinating exploration of probability, revealing
traps and fallacies in the field. It describes
and analyses a remarkable variety of situations
where chance plays a role,
including football pools, the Lottery, TV
games, sport, cards, roulette, coins, and
dice. The book guides the reader round common
pitfalls, demonstrates how to make better
informed decisions, and shows where
the odds can be unexpectedly in your favour.
Contents/
What is probability
The National Lottery
Football Pools
Premium Bonds
Dice
Coins
Roulette
Matrix games
Matching Problems
TV shows
Benford's Law
Best of n
Card games
Bookies, the Tote, Spread betting
Miscellaneous applications in sport
Appendices
Hardback, 0-19-850292-3
344 pages, line illustrations, 216mm x 138mm
Publication date: 4 March 1999
This volume introduces smoothing techniques
(splines and kernels) necessary for non-
and semi-parametric regression. Rather than
merely addressing one approach, it presents
the theory, computation, and application
of a variety of approaches to multivariate
regression problems, occasionally comparing
them with competing univariate and multivariate
smoothing techniques.
Table of Contents
Spline Regression (R. Eubank).
Variance Estimation and Smoothing Parameter
Selection for Spline Regression (A. Van Der
Linde).
Kernel Regression (P. Sarda & P. Vieu).
Variance Estimation and Bandwidth Selection
for Kernel Regression (E. Herrmann).
Spline and Kernel Regression under Shape
Restrictions (M. Delecroix & C. Thomas-Agnan).
Spline and Kernel Regression for Dependent
Data (R. Kohn, et al.).
Wavelets for Regression and Other Statistical
Problems (G. Nason & B. Silverman).
Smoothing Methods for Discrete Data (J. Simonoff
& G. Tutz).
Local Polynomial Fitting (J. Fan & I.
Gijbels).
Additive and Generalized Additive Models
(M. Schimek & B. Turlach).
Multivariate Spline Regression (C. Gu).
Multivariate and Semiparametric Kernel Regression
(W. Hardle & M. Muller).
Spatial Process Estimates as Smoothers (D.
Nychka).
Resampling Methods for Nonparametric Regression
(E. Mammen).
Multidimensional Smoothing and Visualization
(D. Scott).
Projection Pursuit Regression (J. Grassmann
& S. Klinke).
Sliced Inverse Regression (T. Kotter).
Dynamic and Semiparametric Models (L. Fahmeir
& L. Knorr-Held).
Nonparametric Bayesian Bivariate Surface
Estimation (M. Smith, et al.).
Subject: Statistics / Regression /
ISBN: 0-471-17946-9
Hardcover
Price: US$125.00 est.
Projected Pub Date: Jan 2000
Series Title:
Wiley Series in Probability and Mathematical
Statistics - Applied Probability and Statistics
Section
The classic reference on the theory and application
of random data analysis?now expanded and
revised This eagerly awaited new edition
of the bestselling random data analysis book
continues to provide first-rate, practical
tools for scientists and engineers who investigate
dynamic data as well as those who use statistical
methods to solve engineering problems. It
is fully updated, covering new procedures
developed since 1986 and extending the discussion
to a remarkably broad range of applied fields,
from aerospace and automotive industries
to biomedical research. Comprehensive and
self-contained, this new edition also greatly
expands coverage of the theory, including
derivations of key relationships in probability
and random process theory not usually found
in books of this kind. Special features of
Random Data: Analysis and Measurement Procedures,
Third Edition include:
Basic probability functions for level crossings
and peak values of random data
Complete derivations of both old and new
practical formulas for statistical error
analysis of computed estimates
The latest methods for data acquisition and
processing as well as nonstationary data
analysis
Additional techniques on digital data analysis
procedures
New material on the analysis of multiple-input/multiple-output
linear systems
Numerous new examples and problem sets
Hundreds of updated illustrations and references
JULIUS S. BENDAT, PhD, is President of the
J. S. Bendat Company and the
author of Nonlinear System Techniques and
Applications (available from Wiley).
ALLAN G. PIERSOL, PE, is President of Piersol
Engineering Company and the author of several
chapters in engineering handbooks. The authors
have previously collaborated on the companion
volume to this book, Engineering Applications
of Correlation and Spectral Analysis, Second
Edition, also available from Wiley.
Table of Contents
asic Descriptions and Properties.
Linear Physical Systems.
Probability Fundamentals.
Statistical Principles.
Stationary Random Processes.
Single-Input/Output Relationships.
Multiple-Input/Output Relationships.
Statistical Errors in Basic Estimates.
Statistical Errors in Advanced Estimates.
Data Acquisition and Processing.
Digital Data Analysis.
Nonstationary Data Analysis.
The Hilbert Transform.
Appendices.
Subject: Statistics / Data Analysis and Management
/
ISBN: 0-471-31733-0
Hardcover
Projected Pub Date: Jan 2000
Series Title: Wiley Series in Probability
and Statistics: Texts and References Section
This unique book combines lucid and engaging
exposition, graphic and poignantly applied
examples, and realistic exercises to take
readers beyond the mechanics of statistical
techniques. The result is a journey into
the realm of practical data analysis and
inference-based problem solving.
Table of Contents
hat is Statistics?
Tools for Exploring Univariate Data.
Exploratory Tools for Relationships.
Probabilities and Proportions.
Discrete Random Variables.
Continuous Random Variables.
Sampling Distributions of Estimates.
Confidence Intervals.
Significance Testing: Using Data to Test
Hypotheses.
Data on a Continuous Variable.
Tables of Counts.
Relationships Between Quantitative Variables:
Regression and Correlation.
Control Charts.
Time Series.
Appendices.
References.
Answers to Selected Exercises.
Index.
Subject: Statistics / General & Introductory
Statistics /
ISBN: 0-471-32936-3
Hardcover
Published: Nov 1999