Modeling with Data :Tools and Techniques for Scientific Computing

Publication subTitle :Tools and Techniques for Scientific Computing

Author: Klemens Ben  

Publisher: Princeton University Press‎

Publication year: 2008

E-ISBN: 9781400828746

P-ISBN(Paperback): 9780691133140

Subject: O212 Statistics

Keyword: 数理科学和化学,自动化技术、计算机技术

Language: ENG

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Description

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.

Chapter

2.7 Arrays and other pointer tricks

2.8 Strings

2.9 (Omitted) Errors

Chapter 3. Databases

3.1 Basic queries

3.2 (Omitted) Doing more with queries

3.3 Joins and subqueries

3.4 On database design

3.5 Folding queries into C code

3.6 Maddening details

3.7 Some examples

Chapter 4. Matrices and models

4.1 The GSL's matrices and vectors

4.2 apop_data

4.3 Shunting data

4.4 Linear algebra

4.5 Numbers

4.6 (Omitted) gsl_matrix and gsl_vector internals

4.7 Models

Chapter 5. Graphics

5.1 plot

5.2 (Omitted) Some common settings

5.3 From arrays to plots

5.4 A sampling of special plots

5.5 Animation

5.6 On producing good plots

5.7 (Omitted) Graphs—nodes and flowcharts

5.8 (Omitted) Printing and LATEX

Chapter 6. (Omitted) More coding tools

6.1 Function pointers

6.2 Data structures

6.3 Parameters

6.4 (Omitted) Syntactic sugar

6.5 More tools

PART II: STATISTICS

Chapter 7. Distributions for description

7.1 Moments

7.2 Sample distributions

7.3 Using the sample distributions

7.4 Non-parametric description

Chapter 8. Linear projections

8.1 (Omitted) Principal component analysis

8.2 OLS and friends

8.3 Discrete variables

8.4 Multilevel modeling

Chapter 9. Hypothesis testing with the CLT

9.1 The Central Limit Theorem

9.2 Meet the Gaussian family

9.3 Testing a hypothesis

9.4 ANOVA

9.5 Regression

9.6 Goodness of fit

Chapter 10. Maximum likelihood estimation

10.1 Log likelihood and friends

10.2 Description: Maximum likelihood estimators

10.3 Missing data

10.4 Testing with likelihoods

Chapter 11. Monte Carlo

11.1 Random number generation

11.2 Description: Finding statistics for a distribution

11.3 Inference: Finding statistics for a parameter

11.4 Drawing a distribution

11.5 Non-parametric testing

Appendix A: Environments and makefiles

A.1 Environment variables

A.2 Paths

A.3 Make

Appendix B: Text processing

B.1 Shell scripts

B.2 Some tools for scripting

B.3 Regular expressions

B.4 Adding and deleting

B.5 More examples

Appendix C: Glossary

Bibliography

Index

A

B

C

D

E

F

G

H

I

J

K

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M

N

O

P

Q

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