Modeling with Data

Tools and Techniques for Scientific Computing

By: Ben Klemens

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Hardcover | 5 January 2009

At a Glance

Hardcover
472 Pages

Dimensions(cm)
25.4 x 17.7 x 254

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Modeling with Data fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results.

Ben Klemens introduces a set of open and unlimited tools, and uses them to demonstrate data management, analysis, and simulation techniques essential for dealing with large data sets and computationally intensive procedures. He then demonstrates how to easily apply these tools to the many threads of statistical technique, including classical, Bayesian, maximum likelihood, and Monte Carlo methods. Klemens's accessible survey describes these models in a unified and nontraditional manner, providing alternative ways of looking at statistical concepts that often befuddle students. The book includes nearly one hundred sample programs of all kinds. Links to these programs will be available on this page at a later date.

Modeling with Data will interest anyone looking for a comprehensive guide to these powerful statistical tools, including researchers and graduate students in the social sciences, biology, engineering, economics, and applied mathematics.

Industry Reviews

"This book presents an original, cheap and powerful solution to the problem of analysis of large data sets... The book is devoted mainly to the practitioner of Statistics, but is also useful to mathematicians, computer scientists, researchers and students in the biology, economics and social sciences."--Radu Trimbitas, StudiaUBB

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You Can Find This Book In

Non-Fiction Mathematics Probability & Statistics Computing & I.T.Graphical & Digital Media Applications 3D Graphics & Modelling Business Applications Mathematical & Statistical Software Computer Programming & Software Development Database Programming Databases Database Design & Theory

Reference, Information & Interdisciplinary Subjects Research & Information Data Analysis

Preface	p. xi
Statistics in the modern day	p. 1
Computing	p. 15
C	p. 17
Lines	p. 18
Variables and their declarations	p. 28
Functions	p. 34
The debugger	p. 43
Compiling and running	p. 48
Pointers	p. 53
Arrays and other pointer tricks	p. 59
Strings	p. 65
*Errors	p. 69
Databases	p. 74
Basic queries	p. 76
*Doing more with queries	p. 80
Joins and subqueries	p. 87
On database design	p. 94
Folding queries into C code	p. 98
Maddening details	p. 103
Some examples	p. 108
Matrices and models	p. 113
The GSL's matrices and vectors	p. 114
apo_da t120
Shunting data	p. 123
Linear algebra	p. 129
Numbers	p. 135
*gsl_matrixand gsl_ve torinternals	p. 140
Models	p. 143
Graphics	p. 157
plot	p. 160
*Some common settings	p. 163
From arrays to plots	p. 166
A sampling of special plots	p. 171
Animation	p. 177
On producing good plots	p. 180
*Graphs--nodes and flowcharts	p. 182
Printing and LATEX	p. 185
*More coding tools	p. 189
Function pointers	p. 190
Data structures	p. 193
Parameters	p. 203
*Syntactic sugar	p. 210
More tools	p. 214
Statistics	p. 217
Distributions for description	p. 219
Moments	p. 219
Sample distributions	p. 235
Using the sample distributions	p. 252
Non-parametric description	p. 261
Linear projections	p. 264
*Principal component analysis	p. 265
OLS and friends	p. 270
Discrete variables	p. 280
Multilevel modeling	p. 288
Hypothesis testing with the CLT	p. 295
The Central Limit Theorem	p. 297
Meet the Gaussian family	p. 301
Testing a hypothesis	p. 307
ANOVA	p. 312
Regression	p. 315
Goodness of fit	p. 319
Maximum likelihood estimation	p. 325
Log likelihood and friends	p. 326
Description: Maximum likelihood estimators	p. 337
Missing data	p. 345
Testing with likelihoods	p. 348
Monte Carlo	p. 356
Random number generation	p. 357
Description: Finding statistics for a distribution	p. 364
Inference: Finding statistics for a parameter	p. 367
Drawing a distribution	p. 371
Non-parametric testing	p. 375
Environments and makefiles	p. 381
Environment variables	p. 381
Paths	p. 385
Make	p. 387
Text processing	p. 392
Shell scripts	p. 393
Some tools for scripting	p. 398
Regular expressions	p. 403
Adding and deleting	p. 413
More examples	p. 415
Glossary	p. 419
Bibliography	p. 435
Index	p. 443
Table of Contents provided by Publisher. All Rights Reserved.

Modeling with Data

Tools and Techniques for Scientific Computing

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

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