Risk analysis: a quantitative guide
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Chichester [u.a.]
Wiley
2008
|
| Edition: | 3. ed. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Hier auch später erschienene, unveränderte Nachdrucke |
| Physical Description: | XIV, 735 S. graph. Darst. |
| ISBN: | 9780470512845 |
Staff View
MARC
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| 100 | 1 | |a Vose, David |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Risk analysis |b a quantitative guide |c David Vose |
| 250 | |a 3. ed. | ||
| 264 | 1 | |a Chichester [u.a.] |b Wiley |c 2008 | |
| 300 | |a XIV, 735 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
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| 500 | |a Hier auch später erschienene, unveränderte Nachdrucke | ||
| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Monte Carlo method | |
| 650 | 4 | |a Risk assessment |x Mathematical models | |
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Record in the Search Index
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|---|---|
| adam_text | Contents
Preface
xiii
Part
1
Introduction
1
1
Why do a risk analysis?
3
1.1
Moving on from What If
Scenarios
3
1.2
The Risk Analysis Process
5
1.3
Risk Management Options
7
1.4
Evaluating Risk Management Options
10
1.5
Inefficiencies in Transferring Risks to Others
11
1.6
Risk Registers
13
2
Planning a risk analysis
21
2.1
Questions and Motives
21
2.2
Determine the Assumptions that are Acceptable or Required
22
2.3
Time and Timing
23
2.4
You ll Need a Good Risk Analyst or Team
23
3
The quality of a risk analysis
29
3.1
The Reasons Why a Risk Analysis can be Terrible
29
3.2
Communicating the Quality of Data Used in a Risk Analysis
31
3.3
Level of Criticality
34
3.4
The Biggest Uncertainty in a Risk Analysis
35
3.5
Iterate
36
4
Choice of model structure
37
4.1
Software Tools and the Models they Build
37
4.2
Calculation Methods
42
4.3
Uncertainty and Variability
47
4.4
How Monte Carlo Simulation Works
57
4.5
Simulation Modelling
63
5
Understanding and using the results of a risk analysis
67
5.1
Writing a Risk Analysis Report
67
5.2
Explaining a Model s Assumptions
69
viii Contents
5.3
Graphical Presentation of a Model s Results
70
5.4
Statistical Methods of Analysing Results
91
Part
2
Introduction
109
6
Probability mathematics and simulation
115
6.1
Probability Distribution Equations
115
6.2
The Definition of Probability
118
6.3
Probability Rules
119
6.4
Statistical Measures
137
7
Building and running a model
145
7.1
Model Design and Scope
145
7.2
Building Models that are Easy to Check and Modify
146
7.3
Building Models that are Efficient
147
7.4
Most Common Modelling Errors
159
8
Some basic random processes
167
8.1
Introduction
167
8.2
The Binomial Process
167
8.3
The
Poisson
Process
176
8.4
The Hypergeometric Process
183
8.5
Central Limit Theorem
188
8.6
Renewal Processes
190
8.7
Mixture Distributions
193
8.8
Martingales
194
8.9
Miscellaneous Examples
194
9
Data and statistics
207
9.1
Classical Statistics
208
9.2
Bayesian Inference
215
9.3
The Bootstrap
246
9.4
Maximum Entropy Principle
254
9.5
Which Technique Should You Use?
255
9.6
Adding uncertainty in Simple Linear Least-Squares Regression Analysis
256
10
Fitting distributions to data
263
10.1
Analysing the Properties of the Observed Data
264
10.2
Fitting a Non-Parametric Distribution to the Observed Data
269
10.3
Fitting a First-Order Parametric Distribution to Observed Data
281
10.4
Fitting a Second-Order Parametric Distribution to Observed Data
297
11
Sums of random variables
301
11.1
The Basic Problem
301
11.2
Aggregate Distributions
305
Contents ix
12
Forecasting with uncertainty
321
12.1
The Properties of a Time Series Forecast
322
12.2
Common Financial Time Series Models
327
12.3
Autoregressive
Models
335
12.4
Markov Chain Models
339
12.5
Birth and Death Models
343
12.6
Time Series Projection of Events Occurring Randomly in Time
345
12.7
Time Series Models with Leading Indicators
348
12.8
Comparing Forecasting Fits for Different Models
351
12.9
Long-Term
Forecasting
352
13
Modelling correlation and dependencies
353
13.1
Introduction
353
13.2
Rank Order Correlation
356
13.3
Copulas
367
13.4
The Envelope Method
380
13.5
Multiple Correlation Using a Look-Up Table
391
14
Eliciting from expert opinion
393
14.1
Introduction
393
14.2
Sources of Error in Subjective Estimation
394
14.3
Modelling Techniques
401
14.4
Calibrating Subject Matter Experts
412
14.5
Conducting
a
Brainstorming
Session
414
14.6
Conducting the Interview
416
15
Testing and modelling causal relationships
423
15.1
Campylobacter Example
424
15.2
Types of Model to Analyse Data
426
15.3
From Risk Factors to Causes
427
15.4
Evaluating Evidence
429
15.5
The Limits of Causal Arguments
429
15.6
An Example of a Qualitative Causal Analysis
430
15.7
Is Causal Analysis Essential?
434
16
Optimisation in risk analysis
435
16.1
Introduction
435
16.2
Optimisation Methods
436
16.3
Risk Analysis Modelling and Optimisation
439
16.4
Working Example: Optimal Allocation of Mineral Pots
444
17
Checking and validating a model
451
17.1
Spreadsheet Model Errors
451
17.2
Checking Model Behaviour
456
17.3
Comparing Predictions Against Reality
460
X
Contents
18
Discounted cashflow modelling
461
18.1
Useful Time Series Models of Sales and Market Size
463
18.2
Summing Random Variables
466
18.3
Summing Variable Margins on Variable Revenues
467
18.4
Financial Measures in Risk Analysis
469
19
Project risk analysis
473
19.1
Cost Risk Analysis
474
19.2
Schedule Risk Analysis
478
19.3
Portfolios of risks
486
19.4
Cascading Risks
487
20
Insurance and finance risk analysis modelling
493
20.1
Operational Risk Modelling
493
20.2
Credit Risk
494
20.3
Credit Ratings and Markov Chain Models
499
20.4
Other Areas of Financial Risk
503
20.5
Measures of Risk
503
20.6
Term Life Insurance
506
20.7
Accident Insurance
509
20.8
Modelling a Correlated Insurance Portfolio
511
20.9
Modelling Extremes
512
20.10
Premium Calculations
513
21
Microbial food safety risk assessment
517
21.1
Growth and Attenuation Models
519
21.2
Dose-Response Models
527
21.3
Is Monte Carlo Simulation the Right Approach?
532
21.4
Some Model Simplifications
533
22
Animal import risk assessment
537
22.1
Testing for an Infected Animal
539
22.2
Estimating True Prevalence in a Population
544
22.3
Importing Problems
553
22.4
Confidence of Detecting an Infected Group
556
22.5
Miscellaneous Animal Health and Food Safety Problems
559
I Guide for lecturers
567
II About ModelRisk
569
III A compendium of distributions
585
ПІЛ
Discrete and Continuous Distributions
585
111.2 Bounded and Unbounded Distributions
586
111.3 Parametric and Non-Parametric Distributions
587
111.4 Univariate and Multivariate Distributions
588
Contents xi
111.5
Lists of Applications and the Most Useful Distributions
588
111.6 How to Read Probability Distribution Equations
593
111.7
The Distributions
599
ΙΠ.8
Introduction to Creating Your Own Distributions
696
III.9 Approximation of One Distribution with Another
703
ШЛО
Recursive Formulae for Discrete Distributions
710
III.
11
A Visual Observation On The Behaviour Of Distributions
713
IV Further reading
715
V
Vose
Consulting
721
References
725
Index
729
|
| adam_txt |
Contents
Preface
xiii
Part
1
Introduction
1
1
Why do a risk analysis?
3
1.1
Moving on from "What If
"
Scenarios
3
1.2
The Risk Analysis Process
5
1.3
Risk Management Options
7
1.4
Evaluating Risk Management Options
10
1.5
Inefficiencies in Transferring Risks to Others
11
1.6
Risk Registers
13
2
Planning a risk analysis
21
2.1
Questions and Motives
21
2.2
Determine the Assumptions that are Acceptable or Required
22
2.3
Time and Timing
23
2.4
You'll Need a Good Risk Analyst or Team
23
3
The quality of a risk analysis
29
3.1
The Reasons Why a Risk Analysis can be Terrible
29
3.2
Communicating the Quality of Data Used in a Risk Analysis
31
3.3
Level of Criticality
34
3.4
The Biggest Uncertainty in a Risk Analysis
35
3.5
Iterate
36
4
Choice of model structure
37
4.1
Software Tools and the Models they Build
37
4.2
Calculation Methods
42
4.3
Uncertainty and Variability
47
4.4
How Monte Carlo Simulation Works
57
4.5
Simulation Modelling
63
5
Understanding and using the results of a risk analysis
67
5.1
Writing a Risk Analysis Report
67
5.2
Explaining a Model's Assumptions
69
viii Contents
5.3
Graphical Presentation of a Model's Results
70
5.4
Statistical Methods of Analysing Results
91
Part
2
Introduction
109
6
Probability mathematics and simulation
115
6.1
Probability Distribution Equations
115
6.2
The Definition of "Probability"
118
6.3
Probability Rules
119
6.4
Statistical Measures
137
7
Building and running a model
145
7.1
Model Design and Scope
145
7.2
Building Models that are Easy to Check and Modify
146
7.3
Building Models that are Efficient
147
7.4
Most Common Modelling Errors
159
8
Some basic random processes
167
8.1
Introduction
167
8.2
The Binomial Process
167
8.3
The
Poisson
Process
176
8.4
The Hypergeometric Process
183
8.5
Central Limit Theorem
188
8.6
Renewal Processes
190
8.7
Mixture Distributions
193
8.8
Martingales
194
8.9
Miscellaneous Examples
194
9
Data and statistics
207
9.1
Classical Statistics
208
9.2
Bayesian Inference
215
9.3
The Bootstrap
246
9.4
Maximum Entropy Principle
254
9.5
Which Technique Should You Use?
255
9.6
Adding uncertainty in Simple Linear Least-Squares Regression Analysis
256
10
Fitting distributions to data
263
10.1
Analysing the Properties of the Observed Data
264
10.2
Fitting a Non-Parametric Distribution to the Observed Data
269
10.3
Fitting a First-Order Parametric Distribution to Observed Data
281
10.4
Fitting a Second-Order Parametric Distribution to Observed Data
297
11
Sums of random variables
301
11.1
The Basic Problem
301
11.2
Aggregate Distributions
305
Contents ix
12
Forecasting with uncertainty
321
12.1
The Properties of a Time Series Forecast
322
12.2
Common Financial Time Series Models
327
12.3
Autoregressive
Models
335
12.4
Markov Chain Models
339
12.5
Birth and Death Models
343
12.6
Time Series Projection of Events Occurring Randomly in Time
345
12.7
Time Series Models with Leading Indicators
348
12.8
Comparing Forecasting Fits for Different Models
351
12.9
Long-Term
Forecasting
352
13
Modelling correlation and dependencies
353
13.1
Introduction
353
13.2
Rank Order Correlation
356
13.3
Copulas
367
13.4
The Envelope Method
380
13.5
Multiple Correlation Using a Look-Up Table
391
14
Eliciting from expert opinion
393
14.1
Introduction
393
14.2
Sources of Error in Subjective Estimation
394
14.3
Modelling Techniques
401
14.4
Calibrating Subject Matter Experts
412
14.5
Conducting
a
Brainstorming
Session
414
14.6
Conducting the Interview
416
15
Testing and modelling causal relationships
423
15.1
Campylobacter Example
424
15.2
Types of Model to Analyse Data
426
15.3
From Risk Factors to Causes
427
15.4
Evaluating Evidence
429
15.5
The Limits of Causal Arguments
429
15.6
An Example of a Qualitative Causal Analysis
430
15.7
Is Causal Analysis Essential?
434
16
Optimisation in risk analysis
435
16.1
Introduction
435
16.2
Optimisation Methods
436
16.3
Risk Analysis Modelling and Optimisation
439
16.4
Working Example: Optimal Allocation of Mineral Pots
444
17
Checking and validating a model
451
17.1
Spreadsheet Model Errors
451
17.2
Checking Model Behaviour
456
17.3
Comparing Predictions Against Reality
460
X
Contents
18
Discounted cashflow modelling
461
18.1
Useful Time Series Models of Sales and Market Size
463
18.2
Summing Random Variables
466
18.3
Summing Variable Margins on Variable Revenues
467
18.4
Financial Measures in Risk Analysis
469
19
Project risk analysis
473
19.1
Cost Risk Analysis
474
19.2
Schedule Risk Analysis
478
19.3
Portfolios of risks
486
19.4
Cascading Risks
487
20
Insurance and finance risk analysis modelling
493
20.1
Operational Risk Modelling
493
20.2
Credit Risk
494
20.3
Credit Ratings and Markov Chain Models
499
20.4
Other Areas of Financial Risk
503
20.5
Measures of Risk
503
20.6
Term Life Insurance
506
20.7
Accident Insurance
509
20.8
Modelling a Correlated Insurance Portfolio
511
20.9
Modelling Extremes
512
20.10
Premium Calculations
513
21
Microbial food safety risk assessment
517
21.1
Growth and Attenuation Models
519
21.2
Dose-Response Models
527
21.3
Is Monte Carlo Simulation the Right Approach?
532
21.4
Some Model Simplifications
533
22
Animal import risk assessment
537
22.1
Testing for an Infected Animal
539
22.2
Estimating True Prevalence in a Population
544
22.3
Importing Problems
553
22.4
Confidence of Detecting an Infected Group
556
22.5
Miscellaneous Animal Health and Food Safety Problems
559
I Guide for lecturers
567
II About ModelRisk
569
III A compendium of distributions
585
ПІЛ
Discrete and Continuous Distributions
585
111.2 Bounded and Unbounded Distributions
586
111.3 Parametric and Non-Parametric Distributions
587
111.4 Univariate and Multivariate Distributions
588
Contents xi
111.5
Lists of Applications and the Most Useful Distributions
588
111.6 How to Read Probability Distribution Equations
593
111.7
The Distributions
599
ΙΠ.8
Introduction to Creating Your Own Distributions
696
III.9 Approximation of One Distribution with Another
703
ШЛО
Recursive Formulae for Discrete Distributions
710
III.
11
A Visual Observation On The Behaviour Of Distributions
713
IV Further reading
715
V
Vose
Consulting
721
References
725
Index
729 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Vose, David |
| author_facet | Vose, David |
| author_role | aut |
| author_sort | Vose, David |
| author_variant | d v dv |
| building | Verbundindex |
| bvnumber | BV023011557 |
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| classification_rvk | QC 020 SK 980 |
| ctrlnum | (OCoLC)174112755 (DE-599)BVBBV023011557 |
| dewey-full | 658.4/0352 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.4/0352 |
| dewey-search | 658.4/0352 |
| dewey-sort | 3658.4 3352 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Mathematik Wirtschaftswissenschaften |
| edition | 3. ed. |
| format | Book |
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| id | DE-604.BV023011557 |
| illustrated | Illustrated |
| index_date | 2024-07-02T19:09:36Z |
| indexdate | 2024-07-09T21:08:55Z |
| institution | BVB |
| isbn | 9780470512845 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016215766 |
| oclc_num | 174112755 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG DE-945 DE-703 DE-355 DE-BY-UBR DE-384 DE-92 DE-N2 DE-29T DE-706 DE-19 DE-BY-UBM DE-188 |
| owner_facet | DE-473 DE-BY-UBG DE-945 DE-703 DE-355 DE-BY-UBR DE-384 DE-92 DE-N2 DE-29T DE-706 DE-19 DE-BY-UBM DE-188 |
| physical | XIV, 735 S. graph. Darst. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Wiley |
| record_format | marc |
| spelling | Vose, David Verfasser aut Risk analysis a quantitative guide David Vose 3. ed. Chichester [u.a.] Wiley 2008 XIV, 735 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Hier auch später erschienene, unveränderte Nachdrucke Mathematisches Modell Monte Carlo method Risk assessment Mathematical models Wahrscheinlichkeitstheorie (DE-588)4079013-7 gnd rswk-swf Risikoanalyse (DE-588)4137042-9 gnd rswk-swf Stochastisches Modell (DE-588)4057633-4 gnd rswk-swf Risikoanalyse (DE-588)4137042-9 s Stochastisches Modell (DE-588)4057633-4 s Wahrscheinlichkeitstheorie (DE-588)4079013-7 s DE-604 Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016215766&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Vose, David Risk analysis a quantitative guide Mathematisches Modell Monte Carlo method Risk assessment Mathematical models Wahrscheinlichkeitstheorie (DE-588)4079013-7 gnd Risikoanalyse (DE-588)4137042-9 gnd Stochastisches Modell (DE-588)4057633-4 gnd |
| subject_GND | (DE-588)4079013-7 (DE-588)4137042-9 (DE-588)4057633-4 |
| title | Risk analysis a quantitative guide |
| title_auth | Risk analysis a quantitative guide |
| title_exact_search | Risk analysis a quantitative guide |
| title_exact_search_txtP | Risk analysis a quantitative guide |
| title_full | Risk analysis a quantitative guide David Vose |
| title_fullStr | Risk analysis a quantitative guide David Vose |
| title_full_unstemmed | Risk analysis a quantitative guide David Vose |
| title_short | Risk analysis |
| title_sort | risk analysis a quantitative guide |
| title_sub | a quantitative guide |
| topic | Mathematisches Modell Monte Carlo method Risk assessment Mathematical models Wahrscheinlichkeitstheorie (DE-588)4079013-7 gnd Risikoanalyse (DE-588)4137042-9 gnd Stochastisches Modell (DE-588)4057633-4 gnd |
| topic_facet | Mathematisches Modell Monte Carlo method Risk assessment Mathematical models Wahrscheinlichkeitstheorie Risikoanalyse Stochastisches Modell |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016215766&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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