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Frederick I Moxley - One of the best experts on this subject based on the ideXlab platform.
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intelligent adversary Risk Analysis a bioterrorism Risk management model
Risk Analysis, 2010Co-Authors: Gregory S. Parnell, Christopher M Smith, Frederick I MoxleyAbstract:The tragic events of 9/11 and the concerns about the potential for a terrorist or hostile state attack with weapons of mass destruction have led to an increased emphasis on Risk Analysis for homeland security. Uncertain hazards (natural and engineering) have been successfully analyzed using probabilistic Risk Analysis (PRA). Unlike uncertain hazards, terrorists and hostile states are intelligent adversaries who can observe our vulnerabilities and dynamically adapt their plans and actions to achieve their objectives. This article compares uncertain hazard Risk Analysis with intelligent adversary Risk Analysis, describes the intelligent adversary Risk Analysis challenges, and presents a probabilistic defender-attacker-defender model to evaluate the baseline Risk and the potential Risk reduction provided by defender investments. The model includes defender decisions prior to an attack; attacker decisions during the attack; defender actions after an attack; and the uncertainties of attack implementation, detection, and consequences. The Risk management model is demonstrated with an illustrative bioterrorism problem with notional data.
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intelligent adversary Risk Analysis a bioterrorism Risk management model
Risk Analysis, 2010Co-Authors: Gregory S. Parnell, Christopher M Smith, Frederick I MoxleyAbstract:The tragic events of 9/11 and the concerns about the potential for a terrorist or hostile state attack with weapons of mass destruction have led to an increased emphasis on Risk Analysis for homeland security. Uncertain hazards (natural and engineering) have been successfully analyzed using probabilistic Risk Analysis (PRA). Unlike uncertain hazards, terrorists and hostile states are intelligent adversaries who can observe our vulnerabilities and dynamically adapt their plans and actions to achieve their objectives. This article compares uncertain hazard Risk Analysis with intelligent adversary Risk Analysis, describes the intelligent adversary Risk Analysis challenges, and presents a probabilistic defender-attacker-defender model to evaluate the baseline Risk and the potential Risk reduction provided by defender investments. The model includes defender decisions prior to an attack; attacker decisions during the attack; defender actions after an attack; and the uncertainties of attack implementation, detection, and consequences. The Risk management model is demonstrated with an illustrative bioterrorism problem with notional data. Language: en
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intelligent adversary Risk Analysis a bioterrorism Risk management model preprint
2009Co-Authors: Gregory S. Parnell, Christopher M Smith, Frederick I MoxleyAbstract:Abstract : The tragic events of 911 and the concerns about the potential for a terrorist or hostile state attack with weapons of mass destruction have led to an increased emphasis on Risk Analysis for homeland security. Uncertain hazards (natural and engineering) have been analyzed using Probabilistic Risk Analysis (PRA). Unlike uncertain hazards, terrorists and hostile states are intelligent adversaries who adapt their plans and actions to achieve their strategic objectives. The critical Risk Analysis question addressed in this paper is as follows: Are the standard PRA techniques for uncertain hazard techniques adequate and appropriate for intelligent adversaries? Our answer is an emphatic no. We will show that treating adversary decisions as uncertain hazards is inappropriate because it provides the wrong assessment of Risks. Specifically, the paper compares uncertain hazard Risk Analysis with intelligent adversary Risk Analysis, describes the intelligent adversary Risk Analysis challenges, and uses a defender-attacker-defender decision Analysis model to evaluate defender investments. The model includes defender decisions prior to an attack; attacker decisions during the attack; defender actions after an attack; and the uncertainties of attack implementation, detection, and consequences. In section 1, we describe the difference between natural hazards and intelligent adversaries and demonstrate, with a simple example, that standard PRA does not properly assess the Risk of an intelligent adversary attack. In section 2, we describe a canonical model for resource allocation decision making for an intelligent adversary problem using an illustrative bioterrorism example with notional data. In section 3, we describe the illustrative Analysis results obtained for the model and discuss the insights they provide for Risk management. In section 4, we describe the benefits and limitations of the model. Finally, we discuss future work and our conclusions.
Gregory S. Parnell - One of the best experts on this subject based on the ideXlab platform.
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intelligent adversary Risk Analysis a bioterrorism Risk management model
Risk Analysis, 2010Co-Authors: Gregory S. Parnell, Christopher M Smith, Frederick I MoxleyAbstract:The tragic events of 9/11 and the concerns about the potential for a terrorist or hostile state attack with weapons of mass destruction have led to an increased emphasis on Risk Analysis for homeland security. Uncertain hazards (natural and engineering) have been successfully analyzed using probabilistic Risk Analysis (PRA). Unlike uncertain hazards, terrorists and hostile states are intelligent adversaries who can observe our vulnerabilities and dynamically adapt their plans and actions to achieve their objectives. This article compares uncertain hazard Risk Analysis with intelligent adversary Risk Analysis, describes the intelligent adversary Risk Analysis challenges, and presents a probabilistic defender-attacker-defender model to evaluate the baseline Risk and the potential Risk reduction provided by defender investments. The model includes defender decisions prior to an attack; attacker decisions during the attack; defender actions after an attack; and the uncertainties of attack implementation, detection, and consequences. The Risk management model is demonstrated with an illustrative bioterrorism problem with notional data.
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intelligent adversary Risk Analysis a bioterrorism Risk management model
Risk Analysis, 2010Co-Authors: Gregory S. Parnell, Christopher M Smith, Frederick I MoxleyAbstract:The tragic events of 9/11 and the concerns about the potential for a terrorist or hostile state attack with weapons of mass destruction have led to an increased emphasis on Risk Analysis for homeland security. Uncertain hazards (natural and engineering) have been successfully analyzed using probabilistic Risk Analysis (PRA). Unlike uncertain hazards, terrorists and hostile states are intelligent adversaries who can observe our vulnerabilities and dynamically adapt their plans and actions to achieve their objectives. This article compares uncertain hazard Risk Analysis with intelligent adversary Risk Analysis, describes the intelligent adversary Risk Analysis challenges, and presents a probabilistic defender-attacker-defender model to evaluate the baseline Risk and the potential Risk reduction provided by defender investments. The model includes defender decisions prior to an attack; attacker decisions during the attack; defender actions after an attack; and the uncertainties of attack implementation, detection, and consequences. The Risk management model is demonstrated with an illustrative bioterrorism problem with notional data. Language: en
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intelligent adversary Risk Analysis a bioterrorism Risk management model preprint
2009Co-Authors: Gregory S. Parnell, Christopher M Smith, Frederick I MoxleyAbstract:Abstract : The tragic events of 911 and the concerns about the potential for a terrorist or hostile state attack with weapons of mass destruction have led to an increased emphasis on Risk Analysis for homeland security. Uncertain hazards (natural and engineering) have been analyzed using Probabilistic Risk Analysis (PRA). Unlike uncertain hazards, terrorists and hostile states are intelligent adversaries who adapt their plans and actions to achieve their strategic objectives. The critical Risk Analysis question addressed in this paper is as follows: Are the standard PRA techniques for uncertain hazard techniques adequate and appropriate for intelligent adversaries? Our answer is an emphatic no. We will show that treating adversary decisions as uncertain hazards is inappropriate because it provides the wrong assessment of Risks. Specifically, the paper compares uncertain hazard Risk Analysis with intelligent adversary Risk Analysis, describes the intelligent adversary Risk Analysis challenges, and uses a defender-attacker-defender decision Analysis model to evaluate defender investments. The model includes defender decisions prior to an attack; attacker decisions during the attack; defender actions after an attack; and the uncertainties of attack implementation, detection, and consequences. In section 1, we describe the difference between natural hazards and intelligent adversaries and demonstrate, with a simple example, that standard PRA does not properly assess the Risk of an intelligent adversary attack. In section 2, we describe a canonical model for resource allocation decision making for an intelligent adversary problem using an illustrative bioterrorism example with notional data. In section 3, we describe the illustrative Analysis results obtained for the model and discuss the insights they provide for Risk management. In section 4, we describe the benefits and limitations of the model. Finally, we discuss future work and our conclusions.
Shyiming Chen - One of the best experts on this subject based on the ideXlab platform.
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fuzzy Risk Analysis based on ranking generalized fuzzy numbers with different heights and different spreads
Expert Systems With Applications, 2009Co-Authors: Shyiming Chen, Jimho ChenAbstract:In this paper, we present a new method for fuzzy Risk Analysis based on ranking generalized fuzzy numbers with different heights and different spreads. First, we present a new method for ranking generalized fuzzy numbers. The proposed method considers the defuzzified values, the heights and the spreads for ranking generalized fuzzy numbers. Based on the proposed method for ranking generalized fuzzy numbers, we propose a fuzzy Risk Analysis algorithm to deal with fuzzy Risk Analysis problems. The proposed method provides a useful way for handling the fuzzy Risk Analysis problems.
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Fuzzy Risk Analysis based on the ranking of generalized trapezoidal fuzzy numbers
Applied Intelligence, 2007Co-Authors: Shi-jay Chen, Shyiming ChenAbstract:In this paper, we present a new method for fuzzy Risk Analysis based on the ranking of generalized trapezoidal fuzzy numbers. The proposed method considers the centroid points and the standard deviations of generalized trapezoidal fuzzy numbers for ranking generalized trapezoidal fuzzy numbers. We also use an example to compare the ranking results of the proposed method with the existing centroid-index ranking methods. The proposed ranking method can overcome the drawbacks of the existing centroid-index ranking methods. Based on the proposed ranking method, we also present an algorithm to deal with fuzzy Risk Analysis problems. The proposed fuzzy Risk Analysis algorithm can overcome the drawbacks of the one we presented in [7].
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a new method for ranking generalized fuzzy numbers for handling fuzzy Risk Analysis problems
Joint International Conference on Information Sciences, 2006Co-Authors: Shyiming Chen, Jimho ChenAbstract:In this paper, we present a new method for ranking generalized fuzzy numbers for dealing with fuzzy Risk Analysis problems. The proposed method considers the defuzzified values, the heights and the spreads of generalized fuzzy numbers, simultaneously, for ranking generalized fuzzy numbers. It gets better ranking results to rank generalized fuzzy numbers than the existing methods. We also apply the proposed method to deal with fuzzy Risk Analysis problems. The proposed method provides a useful way for handling fuzzy Risk Analysis problems.
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new methods for subjective mental workload assessment and fuzzy Risk Analysis
Cybernetics and Systems, 1996Co-Authors: Shyiming ChenAbstract:This paper presents new methods for subjective mental workload assess ment and fuzzy Risk Analysis in which simplified fuzzy number arithmetic operations are used for subjective mental workload assessment and fuzzy Risk Analysis The proposed methods can provide useful ways to handle subjective mental workload assessment problems and fuzzy Risk Analysis problems
Christopher M Smith - One of the best experts on this subject based on the ideXlab platform.
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intelligent adversary Risk Analysis a bioterrorism Risk management model
Risk Analysis, 2010Co-Authors: Gregory S. Parnell, Christopher M Smith, Frederick I MoxleyAbstract:The tragic events of 9/11 and the concerns about the potential for a terrorist or hostile state attack with weapons of mass destruction have led to an increased emphasis on Risk Analysis for homeland security. Uncertain hazards (natural and engineering) have been successfully analyzed using probabilistic Risk Analysis (PRA). Unlike uncertain hazards, terrorists and hostile states are intelligent adversaries who can observe our vulnerabilities and dynamically adapt their plans and actions to achieve their objectives. This article compares uncertain hazard Risk Analysis with intelligent adversary Risk Analysis, describes the intelligent adversary Risk Analysis challenges, and presents a probabilistic defender-attacker-defender model to evaluate the baseline Risk and the potential Risk reduction provided by defender investments. The model includes defender decisions prior to an attack; attacker decisions during the attack; defender actions after an attack; and the uncertainties of attack implementation, detection, and consequences. The Risk management model is demonstrated with an illustrative bioterrorism problem with notional data.
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intelligent adversary Risk Analysis a bioterrorism Risk management model
Risk Analysis, 2010Co-Authors: Gregory S. Parnell, Christopher M Smith, Frederick I MoxleyAbstract:The tragic events of 9/11 and the concerns about the potential for a terrorist or hostile state attack with weapons of mass destruction have led to an increased emphasis on Risk Analysis for homeland security. Uncertain hazards (natural and engineering) have been successfully analyzed using probabilistic Risk Analysis (PRA). Unlike uncertain hazards, terrorists and hostile states are intelligent adversaries who can observe our vulnerabilities and dynamically adapt their plans and actions to achieve their objectives. This article compares uncertain hazard Risk Analysis with intelligent adversary Risk Analysis, describes the intelligent adversary Risk Analysis challenges, and presents a probabilistic defender-attacker-defender model to evaluate the baseline Risk and the potential Risk reduction provided by defender investments. The model includes defender decisions prior to an attack; attacker decisions during the attack; defender actions after an attack; and the uncertainties of attack implementation, detection, and consequences. The Risk management model is demonstrated with an illustrative bioterrorism problem with notional data. Language: en
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intelligent adversary Risk Analysis a bioterrorism Risk management model preprint
2009Co-Authors: Gregory S. Parnell, Christopher M Smith, Frederick I MoxleyAbstract:Abstract : The tragic events of 911 and the concerns about the potential for a terrorist or hostile state attack with weapons of mass destruction have led to an increased emphasis on Risk Analysis for homeland security. Uncertain hazards (natural and engineering) have been analyzed using Probabilistic Risk Analysis (PRA). Unlike uncertain hazards, terrorists and hostile states are intelligent adversaries who adapt their plans and actions to achieve their strategic objectives. The critical Risk Analysis question addressed in this paper is as follows: Are the standard PRA techniques for uncertain hazard techniques adequate and appropriate for intelligent adversaries? Our answer is an emphatic no. We will show that treating adversary decisions as uncertain hazards is inappropriate because it provides the wrong assessment of Risks. Specifically, the paper compares uncertain hazard Risk Analysis with intelligent adversary Risk Analysis, describes the intelligent adversary Risk Analysis challenges, and uses a defender-attacker-defender decision Analysis model to evaluate defender investments. The model includes defender decisions prior to an attack; attacker decisions during the attack; defender actions after an attack; and the uncertainties of attack implementation, detection, and consequences. In section 1, we describe the difference between natural hazards and intelligent adversaries and demonstrate, with a simple example, that standard PRA does not properly assess the Risk of an intelligent adversary attack. In section 2, we describe a canonical model for resource allocation decision making for an intelligent adversary problem using an illustrative bioterrorism example with notional data. In section 3, we describe the illustrative Analysis results obtained for the model and discuss the insights they provide for Risk management. In section 4, we describe the benefits and limitations of the model. Finally, we discuss future work and our conclusions.
David Vose - One of the best experts on this subject based on the ideXlab platform.
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Risk Analysis a quantitative guide
2000Co-Authors: David VoseAbstract:Preface. Part 1: Introduction. 1. Why do a Risk Analysis? 1.1. Moving on from "What If" Scenarios. 1.2. The Risk Analysis Process. 1.3. Risk Management Options. 1.4. Evaluating Risk Management Options. 1.5. Inefficiencies in Transferring Risks to Others. 1.6. Risk Registers. 2. Planning a Risk Analysis. 2.1. Questions and Motives. 2.2. Determine the Assumptions that are Acceptable or Required. 2.3. Time and Timing. 2.4. You'll Need a Good Risk Analyst or Team. 3. The quality of a Risk Analysis. 3.1. The Reasons Why a Risk Analysis can be Terrible. 3.2. Communicating the Quality of Data Used in a Risk Analysis. 3.3. Level of Criticality. 3.4. The Biggest Uncertainty in a Risk Analysis. 3.5. Iterate. 4. Choice of model structure. 4.1. Software Tools and the Models they Build. 4.2. Calculation Methods. 4.3. Uncertainty and Variability. 4.4. How Monte Carlo Simulation Works. 4.5. Simulation Modelling. 5. Understanding and using the results of a Risk Analysis. 5.1. Writing a Risk Analysis Report. 5.2. Explaining a Model's Assumptions. 5.3. Graphical Presentation of a Model's Results. 5.4. Statistical Methods of Analysing Results. Part 2: Introduction. 6. Probability mathematics and simulation. 6.1. Probability Distribution Equations. 6.2. The Definition of "Probability". 6.3. Probability Rules. 6.4. Statistical Measures. 7. Building and running a model. 7.1. Model Design and Scope. 7.2. Building Models that are Easy to Check and Modify. 7.3. Building Models that are Efficient. 7.4. Most Common Modelling Errors. 8. Some basic random processes. 8.1. Introduction. 8.2. The Binomial Process. 8.3. The Poisson Process. 8.4. The Hypergeometric Process. 8.5. Central Limit Theorem. 8.6. Renewal Processes. 8.7. Mixture Distributions. 8.8. Martingales. 8.9. Miscellaneous Example. 9. Data and statistics. 9.1. Classical Statistics. 9.2. Bayesian Inference. 9.3. The Bootstrap. 9.4. Maximum Entropy Principle. 9.5. Which Technique Should You Use? 9.6. Adding uncertainty in Simple Linear Least-Squares Regression Analysis. 10. Fitting distributions to data. 10.1. Analysing the Properties of the Observed Data. 10.2. Fitting a Non-Parametric Distribution to the Observed Data. 10.3. Fitting a First-Order Parametric Distribution to Observed Data. 10.4. Fitting a Second-Order Parametric Distribution to Observed Data. 11. Sums of random variables. 11.1. The Basic Problem. 11.2. Aggregate Distributions. 12. Forecasting with uncertainty. 12.1. The Properties of a Time Series Forecast. 12.2. Common Financial Time Series Models. 12.3. Autoregressive Models. 12.4. Markov Chain Models. 12.5. Birth and Death Models. 12.6. Time Series Projection of Events Occurring Randomly in Time. 12.7. Time Series Models with Leading Indicators. 12.8. Comparing Forecasting Fits for Different Models. 12.9. Long-Term Forecasting. 13. Modelling correlation and dependencies. 13.1. Introduction. 13.2. Rank Order Correlation. 13.3. Copulas. 13.4. The Envelope Method. 13.5. Multiple Correlation Using a Look-Up Table. 14. Eliciting from expert opinion. 14.1. Introduction. 14.2. Sources of Error in Subjective Estimation. 14.3. Modelling Techniques. 14.4. Calibrating Subject Matter Experts. 14.5. Conducting a Brainstorming Session. 14.6. Conducting the Interview. 15. Testing and modelling causal relationships. 15.1. Campylobacter Example. 15.2. Types of Model to Analyse Data. 15.3. From Risk Factors to Causes. 15.4. Evaluating Evidence. 15.5. The Limits of Causal Arguments. 15.6. An Example of a Qualitative Causal Analysis. 15.7. Is Causal Analysis Essential? 16. Optimisation in Risk Analysis. 16.1. Introduction. 16.2. Optimisation Methods. 16.3. Risk Analysis Modelling and Optimisation. 16.4. Working Example: Optimal Allocation of Mineral Pots. 17. Checking and validating a model. 17.1. Spreadsheet Model Errors. 17.2. Checking Model Behaviour. 17.3. Comparing Predictions Against Reality. 18. Discounted cashflow modelling. 18.1. Useful Time Series Models of Sales and Market Size. 18.2. Summing Random Variables. 18.3. Summing Variable Margins on Variable Revenues. 18.4. Financial Measures in Risk Analysis. 19. Project Risk Analysis. 19.1. Cost Risk Analysis. 19.2. Schedule Risk Analysis. 19.3. Portfolios of Risks. 19.4. Cascading Risks. 20. Insurance and finance Risk Analysis modelling. 20.1. Operational Risk Modelling. 20.2. Credit Risk. 20.3. Credit Ratings and Markov Chain Models. 20.4. Other Areas of Financial Risk. 20.5. Measures of Risk. 20.6. Term Life Insurance. 20.7. Accident Insurance. 20.8. Modelling a Correlated Insurance Portfolio. 20.9. Modelling Extremes. 20.10. Premium Calculations. 21. Microbial food safety Risk assessment. 21.1. Growth and Attenuation Models. 21.2. Dose-Response Models. 21.3. Is Monte Carlo Simulation the Right Approach? 21.4. Some Model Simplifications. 22. Animal import Risk assessment. 22.1. Testing for an Infected Animal. 22.2. Estimating True Prevalence in a Population. 22.3. Importing Problems. 22.4. Confidence of Detecting an Infected Group. 22.5. Miscellaneous Animal Health and Food Safety Problems. I. Guide for lecturers. II. About ModelRisk. III. A compendium of distributions. III.1. Discrete and Continuous Distributions. III.2. Bounded and Unbounded Distributions. III.3. Parametric and Non-Parametric Distributions. III.4. Univariate and Multivariate Distributions. III.5. Lists of Applications and the Most Useful Distributions. III.6. How to Read Probability Distribution Equations. III.7. The Distributions. III.8. Introduction to Creating Your Own Distributions. III.9. Approximation of One Distribution with Another. III.10. Recursive Formulae for Discrete Distributions. III.11. A Visual Observation On The Behaviour Of Distributions. IV. Further reading. V. Vose Consulting. References. Index.
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Risk Analysis a quantitative guide
2000Co-Authors: David VoseAbstract:Preface. Part 1: Introduction. 1. Why do a Risk Analysis? 1.1. Moving on from "What If" Scenarios. 1.2. The Risk Analysis Process. 1.3. Risk Management Options. 1.4. Evaluating Risk Management Options. 1.5. Inefficiencies in Transferring Risks to Others. 1.6. Risk Registers. 2. Planning a Risk Analysis. 2.1. Questions and Motives. 2.2. Determine the Assumptions that are Acceptable or Required. 2.3. Time and Timing. 2.4. You'll Need a Good Risk Analyst or Team. 3. The quality of a Risk Analysis. 3.1. The Reasons Why a Risk Analysis can be Terrible. 3.2. Communicating the Quality of Data Used in a Risk Analysis. 3.3. Level of Criticality. 3.4. The Biggest Uncertainty in a Risk Analysis. 3.5. Iterate. 4. Choice of model structure. 4.1. Software Tools and the Models they Build. 4.2. Calculation Methods. 4.3. Uncertainty and Variability. 4.4. How Monte Carlo Simulation Works. 4.5. Simulation Modelling. 5. Understanding and using the results of a Risk Analysis. 5.1. Writing a Risk Analysis Report. 5.2. Explaining a Model's Assumptions. 5.3. Graphical Presentation of a Model's Results. 5.4. Statistical Methods of Analysing Results. Part 2: Introduction. 6. Probability mathematics and simulation. 6.1. Probability Distribution Equations. 6.2. The Definition of "Probability". 6.3. Probability Rules. 6.4. Statistical Measures. 7. Building and running a model. 7.1. Model Design and Scope. 7.2. Building Models that are Easy to Check and Modify. 7.3. Building Models that are Efficient. 7.4. Most Common Modelling Errors. 8. Some basic random processes. 8.1. Introduction. 8.2. The Binomial Process. 8.3. The Poisson Process. 8.4. The Hypergeometric Process. 8.5. Central Limit Theorem. 8.6. Renewal Processes. 8.7. Mixture Distributions. 8.8. Martingales. 8.9. Miscellaneous Example. 9. Data and statistics. 9.1. Classical Statistics. 9.2. Bayesian Inference. 9.3. The Bootstrap. 9.4. Maximum Entropy Principle. 9.5. Which Technique Should You Use? 9.6. Adding uncertainty in Simple Linear Least-Squares Regression Analysis. 10. Fitting distributions to data. 10.1. Analysing the Properties of the Observed Data. 10.2. Fitting a Non-Parametric Distribution to the Observed Data. 10.3. Fitting a First-Order Parametric Distribution to Observed Data. 10.4. Fitting a Second-Order Parametric Distribution to Observed Data. 11. Sums of random variables. 11.1. The Basic Problem. 11.2. Aggregate Distributions. 12. Forecasting with uncertainty. 12.1. The Properties of a Time Series Forecast. 12.2. Common Financial Time Series Models. 12.3. Autoregressive Models. 12.4. Markov Chain Models. 12.5. Birth and Death Models. 12.6. Time Series Projection of Events Occurring Randomly in Time. 12.7. Time Series Models with Leading Indicators. 12.8. Comparing Forecasting Fits for Different Models. 12.9. Long-Term Forecasting. 13. Modelling correlation and dependencies. 13.1. Introduction. 13.2. Rank Order Correlation. 13.3. Copulas. 13.4. The Envelope Method. 13.5. Multiple Correlation Using a Look-Up Table. 14. Eliciting from expert opinion. 14.1. Introduction. 14.2. Sources of Error in Subjective Estimation. 14.3. Modelling Techniques. 14.4. Calibrating Subject Matter Experts. 14.5. Conducting a Brainstorming Session. 14.6. Conducting the Interview. 15. Testing and modelling causal relationships. 15.1. Campylobacter Example. 15.2. Types of Model to Analyse Data. 15.3. From Risk Factors to Causes. 15.4. Evaluating Evidence. 15.5. The Limits of Causal Arguments. 15.6. An Example of a Qualitative Causal Analysis. 15.7. Is Causal Analysis Essential? 16. Optimisation in Risk Analysis. 16.1. Introduction. 16.2. Optimisation Methods. 16.3. Risk Analysis Modelling and Optimisation. 16.4. Working Example: Optimal Allocation of Mineral Pots. 17. Checking and validating a model. 17.1. Spreadsheet Model Errors. 17.2. Checking Model Behaviour. 17.3. Comparing Predictions Against Reality. 18. Discounted cashflow modelling. 18.1. Useful Time Series Models of Sales and Market Size. 18.2. Summing Random Variables. 18.3. Summing Variable Margins on Variable Revenues. 18.4. Financial Measures in Risk Analysis. 19. Project Risk Analysis. 19.1. Cost Risk Analysis. 19.2. Schedule Risk Analysis. 19.3. Portfolios of Risks. 19.4. Cascading Risks. 20. Insurance and finance Risk Analysis modelling. 20.1. Operational Risk Modelling. 20.2. Credit Risk. 20.3. Credit Ratings and Markov Chain Models. 20.4. Other Areas of Financial Risk. 20.5. Measures of Risk. 20.6. Term Life Insurance. 20.7. Accident Insurance. 20.8. Modelling a Correlated Insurance Portfolio. 20.9. Modelling Extremes. 20.10. Premium Calculations. 21. Microbial food safety Risk assessment. 21.1. Growth and Attenuation Models. 21.2. Dose-Response Models. 21.3. Is Monte Carlo Simulation the Right Approach? 21.4. Some Model Simplifications. 22. Animal import Risk assessment. 22.1. Testing for an Infected Animal. 22.2. Estimating True Prevalence in a Population. 22.3. Importing Problems. 22.4. Confidence of Detecting an Infected Group. 22.5. Miscellaneous Animal Health and Food Safety Problems. I. Guide for lecturers. II. About ModelRisk. III. A compendium of distributions. III.1. Discrete and Continuous Distributions. III.2. Bounded and Unbounded Distributions. III.3. Parametric and Non-Parametric Distributions. III.4. Univariate and Multivariate Distributions. III.5. Lists of Applications and the Most Useful Distributions. III.6. How to Read Probability Distribution Equations. III.7. The Distributions. III.8. Introduction to Creating Your Own Distributions. III.9. Approximation of One Distribution with Another. III.10. Recursive Formulae for Discrete Distributions. III.11. A Visual Observation On The Behaviour Of Distributions. IV. Further reading. V. Vose Consulting. References. Index.
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quantitative Risk Analysis a guide to monte carlo simulation modelling
1996Co-Authors: David VoseAbstract:Introduction to Monte Carlo Simulation Modelling Probability and Statistics Theory Review A Guide to Probability Distributions Building a Risk Analysis Model Determining Input Distributions from Expert Opinion Determining Input Distributions from Available Data Modelling Dependencies Between Distributions Project Risk Analysis Adding Uncertainty to Forecasts Presenting and Interpreting Risk Analysis Results A Selection of Worked Problems.
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quantitative Risk Analysis a guide to monte carlo simulation modelling
1996Co-Authors: David VoseAbstract:Introduction to Monte Carlo Simulation Modelling Probability and Statistics Theory Review A Guide to Probability Distributions Building a Risk Analysis Model Determining Input Distributions from Expert Opinion Determining Input Distributions from Available Data Modelling Dependencies Between Distributions Project Risk Analysis Adding Uncertainty to Forecasts Presenting and Interpreting Risk Analysis Results A Selection of Worked Problems.
