Decision and forecasting models: with transport applications
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | Undetermined |
| Veröffentlicht: |
New York <<[u.a.]>>
Horwood
1990
|
| Schriftenreihe: | Ellis Horwood Series in transportation
|
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | 468 S. |
| ISBN: | 013929399X |
Internformat
MARC
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| 100 | 1 | |a Jessop, Alan |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Decision and forecasting models |b with transport applications |c Alan Jessop |
| 264 | 1 | |a New York <<[u.a.]>> |b Horwood |c 1990 | |
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| 490 | 0 | |a Ellis Horwood Series in transportation | |
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Datensatz im Suchindex
| _version_ | 1804144521401860096 |
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| adam_text | Contents
Preface : 11
1 INTRODUCTION
1.1 Models 13
1.2 Subjectivity and expertise 14
1.3 System 15
1.4 What can be known? 17
1.5 Opinion 18
1.6 About this book 18
2 PROBABILITY: THE LANGUAGE OF UNCERTAINTY
2.1 Making guesses 20
2.2 Probability 20
2.3 Formal definition 22
2.4 Two or more events 24
2.5 Joint events 29
2.6 Independent events 34
2.7 Exercises 36
3 MORE ABOUT DISTRIBUTIONS
3.1 Two types of variable 38
3.2 Smooth density functions 42
3.3 The cumulative distribution function 46
3.4 A note on interpolation 49
3.5 Using the distribution function to get probabilities 51
3.6 Summary measures for distributions 52
3.7 Measures of location 53
3.8 Measures of dispersion 55
3.9 Credible intervals and confidence intervals 59
3.10 Exercises 64
6 Table of contents
4 TAKING A DECISION (DECISION THEORY)
4.1 Components of a decision 65
4.2 Actions 65
4.3 Outcomes 66
4.4 System states 67
4.5 The payoff table 67
4.6 Decisions without probability assessments 69
4.7 Decisions with probability assessments 72
4.8 Indifference probabilities 73
4.9 Using indifference probabilities 75 j
4.10 Formal representation: utility 77 j
4.11 Coherence 81 j
4.12 Exercises 81 j
I
5 SOME IMPLICATIONS OF BASIC DECISION THEORY j
5.1 Decision trees 83 }
5.2 Sequential decisions 85 I
5.3 Solving the two-stage problem 87 J
5.4 Ranges 93 I
5.5 Sufficient accuracy and probability assessments 93 |
5.6 The value of increased precision 95 j
5.7 Exercises 100 (
6 ALGEBRAIC UTILITY FUNCTIONS •
6.1 The straight line 102 ;
6.2 Logarithms 105
6.3 Some more curvilinear functions Ill
(i) u(x)=x
(»)u(x)=ax
6.4 Graphical interpretation of taking logarithms 121
6.5 The slope of the utility curve 123
6.6 Some common differentials 126
6.7 Exercises 127
7 MORE CALCULUS
7.1 More rules for differentiation 129
(i) Sums of functions
(ii) Products of functions
(iii) Ratios of functions
(iv) Compound functions
7.2 Joining two curves 135
7.3 Integration 136
7.4 Limits: definite and indefinite integrals 140
7.5 The negative exponential distribution 140 r
7.6 A note on discrete variables 143 ,
7.7 Expected utility again 143 I
7.8 Integration by parts 147 J
J
5 Table of contents 7
{ 7.9 Finding an optimal policy by enumeration 149
7.10 Finding an optimal policy analytically 152
7.11 Variance as expected loss 158
; 7.12 Numerical integration 159
; 7.13 Attitudes to risk 165
7.14 Exercises 166
8 INCORPORATING INFORMATION: BAYES RULE
8.1 Bayes Rule: a simple example 168
8.2 Bayes Rule: a more formal description 173
8.3 Estimating a proportion: the binomial distribution as a likelihood. . 174
8.4 Estimating a proportion: the Beta distribution as a prior 181
j 8.5 Using the Beta distribution 186
| 8.6 Estimating a proportion: the posterior distribution 190
8.7 Convergence of estimates 191
8.8 Collecting more than one set of data 192
8.9 Independence of evidence 195
8.10 Exercises 196
9 MORE PROBABILITY DISTRIBUTIONS
9.1 The Poisson distribution 199
9.2 Gamma distribution as a conjugate prior for a Poisson process. . . . 200
9.3 Using the Gamma distribution 203
9.4 The Normal distribution 205
9.5 Tabulations of the Normal distribution ,. 210
9.6 The Normal as a conjugate prior 210
9.7 Sample means 212
9.8 Normal approximations 214
9.9 Exercises 216
10 INFORMATION AND INFORMATION POTENTIAL
10.1 Shannon s information measure 218
10.2 Another way of thinking about information potential 225
10.3 Entropy and EVPI 226
10.4 Jaynes criterion for unbiased priors 227
10.5 Maximum entropy prior: mean given 230
10.6 Finding the root of a function 233
10.7 Maximum entropy prior: mean and variance given 238
10.8 Entropy for distributions of continuous variables 244
10.9 Exercises 244
11 MORE MAXIMUM ENTROPY DISTRIBUTIONS
11.1 Two-dimensional distributions 246
11.2 Two-dimensional distribution: mean given 249
11.3 Information gain 257
11.4 Bayes and Kullback compared 259
11.5 Partial prior information 261
8 Table of contents
11.6 Trip distribution: the gravity model 265
11.7 Trip distribution: updating trip tables 269
11.8 Exercises 274
12 FORECASTING AND MODELLING I
12.1 System and model 276 ;
12.2 Time series and cross-sectional models . ; 278 j
12.3 The forecast period 279
12.4 Time dependent utilities 281
12.5 Exercises 286
13 MOSTLY SIMPLE REGRESSION
13.1 Least squares estimation 287
13.2 Simple linear regression 289
13.3 Model performance: fit 294
13.4 Correlation 296
13.5 Model performance: prediction 300
13.6 Some simple non-linear models 307
13.7 Bayesian combination 312
13.8 Exercises 313
14 TIME SERIES MODELS
14.1 The components of a time series 316
14.2 Smoothing 317
14.3 Three naive forecasts 321
14.4 Measuring forecasting accuracy 322
14.5 Using smoothed series as forecasts 325
14.6 Non-linear trend 328
14.7 Applying modified exponentials 334
14.8 Seasonality 343
14.9 Holt-Winters method 351
14.10 Exercises 356
15 TRACING ERROR AND IMPRECISION
15.1 The nature of the problem 359
15.2 Analytical approach 362
15.3 Simple regression analysis 368
15.4 Implications for modelling 370
15.5 The effects of correlation 372
15.6 Multiple regression 373
15.7 Complexity 380
15.8 Exercises 384
16 RISK ANALYSIS
16.1 Monte Carlo simulation 386
16.2 Random numbers . .387
16.3 Choosing from discrete distributions 392
Table of contents 9
16.4 The inversion method 394
16.5 Other continuous distributions 398
) 16.6 A simple example 400
i 16.7 Sensitivity analysis 403
16.8 Correlation again 404
16.9 Exercises 407
17 SUMMARY AND FURTHER READING
17.1 Contexts for decision 408
17.2 Further reading 410
17.3 References 412
ANSWERS TO NUMERICAL QUESTIONS 416
Appendix 1 SOME FORMULAE FOR PROBABILITY DISTRIBUTIONS .421
Appendix 2 LOGARITHMS AND e
A2.1 Equivalence of logarithms to different bases 422
A2.2 The exponential constant e 423
A2.3 Logarithms to the base 10 423
A2.4 Logarithms to the base e 425
Appendix 3 SOME ALGEBRAIC RESULTS
A3.1 Solving simultaneous linear equations 428
A3.2 Squares of sums and differences 430
A3.3 Solving quadratic equations 431
Appendix 4 DERIVATION OF SIMPSON S RULE 434
Appendix 5 STATISTICAL TABLES
A5.1 General notes 438
A5.2 Binomial distribution 439
A5.3 Poisson distribution 443
A5.4 Normal distribution 448
A5.5 Beta distribution 450
A5.6 Gamma distribution 451
Appendix 6 THE GAMMA FUNCTION 453
Appendix 7 DERIVATION OF THE POISSON DISTRIBUTION 455
Appendix 8 LISTING OF RISK ANALYSIS PROGRAM 456
Index 466
|
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| id | DE-604.BV026089975 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-09T23:04:53Z |
| institution | BVB |
| isbn | 013929399X |
| language | Undetermined |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-021684517 |
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| physical | 468 S. |
| publishDate | 1990 |
| publishDateSearch | 1990 |
| publishDateSort | 1990 |
| publisher | Horwood |
| record_format | marc |
| series2 | Ellis Horwood Series in transportation |
| spelling | Jessop, Alan Verfasser aut Decision and forecasting models with transport applications Alan Jessop New York <<[u.a.]>> Horwood 1990 468 S. txt rdacontent n rdamedia nc rdacarrier Ellis Horwood Series in transportation HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=021684517&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Jessop, Alan Decision and forecasting models with transport applications |
| title | Decision and forecasting models with transport applications |
| title_auth | Decision and forecasting models with transport applications |
| title_exact_search | Decision and forecasting models with transport applications |
| title_full | Decision and forecasting models with transport applications Alan Jessop |
| title_fullStr | Decision and forecasting models with transport applications Alan Jessop |
| title_full_unstemmed | Decision and forecasting models with transport applications Alan Jessop |
| title_short | Decision and forecasting models |
| title_sort | decision and forecasting models with transport applications |
| title_sub | with transport applications |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=021684517&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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