Gespeichert in:
| Beteiligte Personen: | , , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Boca Raton, Fla. [u.a.]
CRC Press
2009
|
| Schriftenreihe: | Chapman & Hall book
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017736533&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017736533&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XIX, 453 S. Ill., graph. Darst. |
| ISBN: | 9781420068726 |
Internformat
MARC
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| 100 | 1 | |a Jones, Owen |e Verfasser |0 (DE-588)134209737 |4 aut | |
| 245 | 1 | 0 | |a Introduction to scientific programming and simulation using R |c Owen Jones ; Robert Maillardet ; Andrew Robinson |
| 264 | 1 | |a Boca Raton, Fla. [u.a.] |b CRC Press |c 2009 | |
| 300 | |a XIX, 453 S. |b Ill., graph. Darst. | ||
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| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
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| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Naturwissenschaft | |
| 650 | 4 | |a Computer programming | |
| 650 | 4 | |a Numerical analysis |x Data processing | |
| 650 | 4 | |a R (Computer program language) | |
| 650 | 4 | |a Science |x Computer simulation | |
| 650 | 4 | |a Science |x Data processing | |
| 650 | 4 | |a Stochastic processes |x Mathematical models | |
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Datensatz im Suchindex
| DE-BY-TUM_call_number | 0102 MAT 650f 2009 A 7000 1002 MAT 650f 2010 A 206 |
|---|---|
| DE-BY-TUM_katkey | 1704134 |
| DE-BY-TUM_location | 01 10 |
| DE-BY-TUM_media_number | 040010157983 040050840781 |
| _version_ | 1821934529816821760 |
| adam_text | Contents
Preface
v
I Programming
1
1
Setting up
3
1.1
Installing
R
3
1.2
Starting
R
3
1.3
Working directory
4
1.4
Writing scripts
5
1.5
Help
5
1.6
Supporting material
5
2
R
as a calculating environment
11
2.1
Arithmetic
11
2.2
Variables
12
2.3
Functions
13
2.4
Vectors
15
2.5
Missing data
18
2.6
Expressions and assignments
19
2.7
Logical expressions
20
2.8
Matrices
23
2.9
The workspace
25
2.10
Exercises
25
CONTENTS
OQ
3
Basic programming
3.1
Introduction 29
3.2
Branching with if 31
3.3
Looping with for
33
3.4
Looping with while
36
3.5
Vector-based programming
38
3.6
Program flow
39
3.7
Basic debugging 41
3.8
Good programming habits
42
3.9
Exercises 43
4
I/O: Input and Output
49
4.1
Text
49
4.2
Input from a file
51
4.3
Input from the keyboard
53
4.4
Output to a file
55
4.5
Plotting
56
4.6
Exercises
58
5
Programming with functions
63
5.1
Functions
63
5.2
Scope and its consequences
68
5.3
Optional arguments and default values
70
5.4
Vector-based programming using functions
70
5.5
Recursive programming
74
5.6
Debugging functions
76
5.7
Exercises
78
6
Sophisticated data structures
85
6.1
Factors
85
6.2 Dataframes 88
6.3
Lists
94
6.4
The apply family
98
6.5
Exercises
105
CONTENTS xv
7
Better graphics
109
7.1
Introduction
109
7.2
Graphics parameters: par 111
7.3
Graphical augmentation
113
7.4
Mathematical typesetting
114
7.5
Permanence
118
7.6
Grouped graphs: lattice
119
7.7
ЗО-ркЛѕ
123
7.8
Exercises
124
8
Pointers to further programming techniques
127
8.1
Packages
127
8.2
Frames and environments
132
8.3
Debugging again
134
8.4
Object-oriented programming:
S3 137
8.5
Object-oriented programming: S4
141
8.6
Compiled code
144
8.7
Further reading
146
8.8
Exercises
146
II Numerical techniques
149
9
Numerical accuracy and program efficiency
151
9.1
Machine representation of numbers
151
9.2
Significant digits
154
9.3
Time
156
9.4
Loops versus vectors
158
9.5
Memory
160
9.6
Caveat
161
9.7
Exercises
162
xvi CONTENTS
10
Root-finding
167
10.1
Introduction
167
10.2
Fixed-point iteration
168
10.3
The Newton-Raphson method
173
10.4
The secant method
176
10.5
The bisection method
178
10.6
Exercises
181
11
Numerical integration
187
11.1
Trapezoidal rule
187
11.2
Simpson s rule
189
11.3
Adaptive quadrature
194
11.4
Exercises
198
12
Optimisation
201
12.1
Newton s method for optimisation
202
12.2
The golden-section method
204
12.3
Multivariate optimisation
207
12.4
Steepest ascent
209
12.5
Newton s method in higher dimensions
213
12.6
Optimisation in
R
and the wider world
218
12.7
A curve fitting example
219
12.8
Exercises
220
III Probability and statistics
225
13
Probability
227
13.1
The probability axioms
227
13.2
Conditional probability
230
13.3
Independence
232
13.4
The Law of Total Probability
233
13.5
Bayes
theorem
234
13.6
Exercises
235
CONTENTS xvii
14 Random variables 241
14.1 Definition
and distribution function
241
14.2
Discrete and continuous random variables
242
14.3
Empirical cdf s and histograms
245
14.4
Expectation and finite approximations
246
14.5
Transformations
251
14.6
Variance and standard deviation
256
14.7
The Weak Law of Large Numbers
257
14.8
Exercises
261
15
Discrete random variables
267
15.1
Discrete random variables in
R
267
15.2
Bernoulli distribution
268
15.3
Binomial distribution
268
15.4
Geometric distribution
270
15.5
Negative binomial distribution
273
15.6
Poisson
distribution
274
15.7
Exercises
277
16
Continuous random variables
281
16.1
Continuous random variables in
R
281
16.2
Uniform distribution
282
16.3
Lifetime models: exponential and Weibull
282
16.4
The
Poisson
process and the gamma distribution
287
16.5
Sampling distributions: normal,
χ2,
and
t
292
16.6
Exercises
297
17
Parameter Estimation
303
17.1
Point Estimation
303
17.2
The Central Limit Theorem
309
17.3
Confidence intervals
314
17.4
Monte-Carlo confidence intervals
321
17.5
Exercises
322
xviii CONTENTS
IV
Simulation 329
18 Simulation 331
18.1
Simulating
iid uniform
samples
331
18.2
Simulating discrete random variables
333
18.3
Inversion method for continuous rv
338
18.4
Rejection method for continuous rv
339
18.5
Simulating normals
345
18.6
Exercises
348
19
Monte-Carlo integration
355
19.1
Hit-and-miss method
355
19.2
(Improved) Monte-Carlo integration
358
19.3
Exercises
360
20
Variance reduction
363
20.1
Antithetic sampling
363
20.2
Importance sampling
367
20.3
Control
variâtes
372
20.4
Exercises
374
21
Case studies
377
21.1
Introduction
377
21.2
Epidemics
378
21.3
Inventory
390
21.4
Seed dispersal
405
22
Student projects
421
22.1
The level of a dam
421
22.2
Roulette
425
22.3
Buffon s needle and cross
428
22.4
Insurance risk
430
22.5
Squash
433
22.6
Stock prices
438
CONTENTS xix
Glossary of R commands
441
Programs and functions developed in the text
447
Index
449
Known for
ite
versatility, the
ітео
programming language
lì
:s widely
osed
for
statistical computing
eind
graphics, but is also a fully functional programming
language well suited to scientific programming.
An Introduction to Scientific Programming and Simulation Using
R
teaches
the skills needed to perform scientific programming while also introducing
stochastic modelling. Stochastic modelling in particular, and mathematical
modelling in general, are intimately linked to scientific programming because the
numerical techniques of scientific programming enable the practical application
of mathematical models to real-world problems.
Following a natural progression that assumes no prior knowledge of programming
or probability, the book is organised into four main sections:
•
Programming in
R
starts with how to obtain and install
R
(for Windows,
MacOS,
and Unix platforms), then tackles basic calculations and program
flow before progressing to function based programming, data structures,
graphics, and object-oriented code
•
A primer on numerical mathematics introduces concepts of numerical
accuracy and program efficiency in the context of root-finding, integration,
and optimization
•
A self-contained introduction to probability theory takes readers as far as
the Weak Law of Large Numbers and the Central Limit Theorem, equipping
them for point and interval estimation
•
Simulation teaches how to generate univariate random variables, do Monte-
Carlo integration, and variance reduction techniques
In the last section, stochastic modelling is introduced using extensive case
studies on epidemics, inventory management, and plant dispersal. A tried and
tested pedagogic approach is employed throughout, with numerous examples,
exercises, and a suite of practice projects.
Unlike most guides to R, this volume is not about the application of statistical
techniques, but rather shows how to turn algorithms into code. It is for those who
want to make tools, not just use them.
|
| any_adam_object | 1 |
| author | Jones, Owen Maillardet, Robert Robinson, Andrew |
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| dewey-tens | 500 - Natural sciences and mathematics |
| discipline | Allgemeine Naturwissenschaft Informatik Mathematik |
| format | Book |
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| id | DE-604.BV035682281 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T13:40:33Z |
| institution | BVB |
| isbn | 9781420068726 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017736533 |
| oclc_num | 418707479 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM DE-703 DE-M49 DE-BY-TUM DE-11 DE-N2 DE-578 DE-945 DE-739 |
| owner_facet | DE-91G DE-BY-TUM DE-703 DE-M49 DE-BY-TUM DE-11 DE-N2 DE-578 DE-945 DE-739 |
| physical | XIX, 453 S. Ill., graph. Darst. |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | CRC Press |
| record_format | marc |
| series2 | Chapman & Hall book |
| spellingShingle | Jones, Owen Maillardet, Robert Robinson, Andrew Introduction to scientific programming and simulation using R Datenverarbeitung Mathematisches Modell Naturwissenschaft Computer programming Numerical analysis Data processing R (Computer program language) Science Computer simulation Science Data processing Stochastic processes Mathematical models Simulation (DE-588)4055072-2 gnd Wissenschaftliches Rechnen (DE-588)4338507-2 gnd R Programm (DE-588)4705956-4 gnd |
| subject_GND | (DE-588)4055072-2 (DE-588)4338507-2 (DE-588)4705956-4 |
| title | Introduction to scientific programming and simulation using R |
| title_auth | Introduction to scientific programming and simulation using R |
| title_exact_search | Introduction to scientific programming and simulation using R |
| title_full | Introduction to scientific programming and simulation using R Owen Jones ; Robert Maillardet ; Andrew Robinson |
| title_fullStr | Introduction to scientific programming and simulation using R Owen Jones ; Robert Maillardet ; Andrew Robinson |
| title_full_unstemmed | Introduction to scientific programming and simulation using R Owen Jones ; Robert Maillardet ; Andrew Robinson |
| title_short | Introduction to scientific programming and simulation using R |
| title_sort | introduction to scientific programming and simulation using r |
| topic | Datenverarbeitung Mathematisches Modell Naturwissenschaft Computer programming Numerical analysis Data processing R (Computer program language) Science Computer simulation Science Data processing Stochastic processes Mathematical models Simulation (DE-588)4055072-2 gnd Wissenschaftliches Rechnen (DE-588)4338507-2 gnd R Programm (DE-588)4705956-4 gnd |
| topic_facet | Datenverarbeitung Mathematisches Modell Naturwissenschaft Computer programming Numerical analysis Data processing R (Computer program language) Science Computer simulation Science Data processing Stochastic processes Mathematical models Simulation Wissenschaftliches Rechnen R Programm |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017736533&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017736533&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT jonesowen introductiontoscientificprogrammingandsimulationusingr AT maillardetrobert introductiontoscientificprogrammingandsimulationusingr AT robinsonandrew introductiontoscientificprogrammingandsimulationusingr |
Teilbibliothek Mathematik & Informatik
| Signatur: | 0102 MAT 650f 2009 A 7000 |
|---|---|
| Exemplar 1 | Ausleihbar Am Standort |
Teilbibliothek Weihenstephan
| Signatur: | 1002 MAT 650f 2010 A 206 |
|---|---|
| Exemplar 1 | Ausleihbar Am Standort |