Administrators use of ModelRisk Cloud
An Administrator is a ModelRisk Cloud User with the authorization to assign access rights to individual Users. An Administrator also has all the access rights of other types of User.
After logging into the system, an administrator will see a view similar to the following:
In addition to the MODEL, PROFILE and HELP menu items that other Users see, an Administrator’s view includes four extra items:
USERS – to manage the individuals who have access to the system
GROUPS – to manage the different Groups into which the Models are collected
MEMBERSHIP – to assign individual Users to one or more Groups
BACKUPS – to backup the set of files stored in the system, or to reset the system to a previous backup
LICENSE INFORMATION – to review the license status and move the system to a different server
USERS section
A User is anyone who has been granted access to the system. The Users section allows an Administrator to add or remove Users from the system. Clicking on USERS in the menu will open a view like this:
One can then do the following:

Add a new User by clicking the button at the top right of the table

Delete one or more Users by selecting their entry using the check boxes, then clicking the Delete selected users button. Deleting a User will not delete any Models, Versions or Results that the User created

Edit the entry for an individual User by clicking the button, for example if the User’s name or email address changes, or to change whether they should have administrative rights

Send an email to the User with a new password by clicking the button if, for example, they are new to the system or have forgotten the password. This can only be done if an email address has already been entered. Note: ModelRisk Cloud employs salted password hashing. Passwords are not visible to anyone, including an Administrator.
GROUPS section
A Group is a collection of Models around a subject, business, or discipline. The purpose of organizing models into Groups is to:

Limit who has access to Models

Make it easy for Users to find the Models they need
The GROUPS section allows an Administrator to add or remove Groups from the system. Clicking on GROUPS in the menu will open a view like this:
One can then do the following:

Add a new Group by clicking the button at the top right of the table

Delete one or more Groups by selecting their entry using the check boxes, then clicking the Delete selected Groups button. Caution: deleting a Group will delete all the Models in that Group, their different Versions and Results

Edit the name or description for an individual Group by clicking the button
MEMBERS section
The MEMBERS section allows an Administrator to add or remove Users from the list of those who have access to each Group. Clicking on MEMBERSHIP in the menu will open a view like this:
One can then adjust the Members of any Group and their access rights by doing the following:
· Select a Group from the dropdown list at the top left of the screen
· Review all the Users who currently are not Members, and who are Members, of that Group (the left and right tables) and move individuals from one list to the other using the arrows (highlighted)
· Change the access rights an individual User enjoys within that Group. There are three levels of access rights:
Models, versions and results – this User has full access to add and remove Models, Versions of those Models, and view any Results. This is typically the rights assigned to a risk modeler
Versions and results – this User can make new Versions of the available Models and review any Results, but cannot add or delete Models from the Group. This is typically the rights assigned to a manager
Results only – this User can access the Results which open within the ResultsViewer application. From there the User can create and modify graphs and tables, save and then distribute these results in PowerPoint, PDF, Excel, Word or ResultsViewer format. This is typically the rights assigned to a business analyst or report writer
BACKUPS section
The BACKUPS section allows an Administrator to make a backup of the complete set of model, results and SID files stored in the ModelRisk Cloud system with one click. It also allows the administrator to restore the system to an earlier set of files.
Backups are stored as a zip file. The window shows a history of all previously made backup zip files that haven’t been deleted. A new backup can be created by clicking the Create Backup button. On completion, it will appear at the bottom of the backup list.
Backups can be downloaded for storage in a separate environment by clicking the Download icon next to the required backup incidence. The system can be restored to a previous backup point by click the restore button next to the required backup incidence.
A backup that has been stored separately can be added back to the backup list (for example, to reset the system) by clicking the Upload backup button, navigating to the location of the backup file, and clicking OK. A backup zipped file will not, however, load to the system if a zip file of the same name already exists in order to avoid overwriting.
Backup zip file structure will look similar to the following:
SID files are stored in a single nested zip folder. Each named Group within the system will have its own zipped folder. The zip file is not password protected, and should be stored in a safe location.LICENSE INFORMATION section
The LICENSE INFORMATION section presents the state of the license. It also provides a button to deactivate the license so that the system can be moved to a new server:
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Navigation
 Risk management
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 More on probability distributions
 Approximating one distribution with another
 Approximations to the Inverse Hypergeometric Distribution
 Normal approximation to the Gamma Distribution
 Normal approximation to the Poisson Distribution
 Approximations to the Hypergeometric Distribution
 Stirlings formula for factorials
 Normal approximation to the Beta Distribution
 Approximation of one distribution with another
 Approximations to the Negative Binomial Distribution
 Normal approximation to the Studentt Distribution
 Approximations to the Binomial Distribution
 Normal_approximation_to_the_Binomial_distribution
 Poisson_approximation_to_the_Binomial_distribution
 Normal approximation to the Chi Squared Distribution
 Recursive formulas for discrete distributions
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 Approximating one distribution with another
 Correlation modeling in risk analysis
 Common mistakes when adapting spreadsheet models for risk analysis
 More advanced risk analysis methods
 SIDs
 Modeling with objects
 ModelRisk database connectivity functions
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 Simulating with ordinary differential equations (ODEs)
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 ModelRisk optimization extension introduction
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 Aggregate modeling in ModelRisk
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 VoseSixSigmaCp
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 Modeling expert opinion
 Modeling expert opinion introduction
 Sources of error in subjective estimation
 Disaggregation
 Distributions used in modeling expert opinion
 A subjective estimate of a discrete quantity
 Incorporating differences in expert opinions
 Modeling opinion of a variable that covers several orders of magnitude
 Maximum entropy
 Probability theory and statistics
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 Stochastic processes
 Stochastic processes introduction
 Poisson process
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 The hypergeometric process
 Number in a sample with a particular characteristic in a hypergeometric process
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 The ChiSquared GoodnessofFit Statistic
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 Censored data
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 Fitting a distribution for a discrete variable
 Fitting a discrete nonparametric secondorder distribution to data
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 Fitting a first order parametric distribution to observed data
 Fitting a discrete nonparametric firstorder distribution to data
 Fitting distributions to data
 Technical subjects
 Comparison of Classical and Bayesian methods
 Comparison of classic and Bayesian estimate of Normal distribution parameters
 Comparison of classic and Bayesian estimate of intensity lambda in a Poisson process
 Comparison of classic and Bayesian estimate of probability p in a binomial process
 Which technique should you use?
 Comparison of classic and Bayesian estimate of mean "time" beta in a Poisson process
 Classical statistics
 Bayesian
 Bootstrap
 The Bootstrap
 Linear regression parametric Bootstrap
 The Jackknife
 Multiple variables Bootstrap Example 2: Difference between two population means
 Linear regression nonparametric Bootstrap
 The parametric Bootstrap
 Bootstrap estimate of prevalence
 Estimating parameters for multiple variables
 Example: Parametric Bootstrap estimate of the mean of a Normal distribution with known standard deviation
 The nonparametric Bootstrap
 Example: Parametric Bootstrap estimate of mean number of calls per hour at a telephone exchange
 The Bootstrap likelihood function for Bayesian inference
 Multiple variables Bootstrap Example 1: Estimate of regression parameters
 Bayesian inference
 Uninformed priors
 Conjugate priors
 Prior distributions
 Bayesian analysis with threshold data
 Bayesian analysis example: gender of a random sample of people
 Informed prior
 Simulating a Bayesian inference calculation
 Hyperparameters
 Hyperparameter example: Microfractures on turbine blades
 Constructing a Bayesian inference posterior distribution in Excel
 Bayesian analysis example: Tigers in the jungle
 Markov chain Monte Carlo (MCMC) simulation
 Introduction to Bayesian inference concepts
 Bayesian estimate of the mean of a Normal distribution with known standard deviation
 Bayesian estimate of the mean of a Normal distribution with unknown standard deviation
 Determining prior distributions for correlated parameters
 Improper priors
 The Jacobian transformation
 Subjective prior based on data
 Taylor series approximation to a Bayesian posterior distribution
 Bayesian analysis example: The Monty Hall problem
 Determining prior distributions for uncorrelated parameters
 Subjective priors
 Normal approximation to the Beta posterior distribution
 Bayesian analysis example: identifying a weighted coin
 Bayesian estimate of the standard deviation of a Normal distribution with known mean
 Likelihood functions
 Bayesian estimate of the standard deviation of a Normal distribution with unknown mean
 Determining a prior distribution for a single parameter estimate
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 Bootstrap
 Comparison of Classical and Bayesian methods
 Analyzing and using data introduction
 Data Object
 Vose probability calculation
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 Excel and ModelRisk model design and validation techniques
 Using range names for model clarity
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 Compare with known answers
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 Model Validation and behavior introduction
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 Split up complex formulas (megaformulas)
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 Model errors
 Model design introduction
 About array functions in Excel
 Excel and ModelRisk model design and validation techniques
 Monte Carlo simulation
 RISK ANALYSIS SOFTWARE
 Risk analysis software from Vose Software
 ModelRisk  risk modeling in Excel
 ModelRisk functions explained
 VoseCopulaOptimalFit and related functions
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 Generalized Pareto Distribution (GPD)
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 More on Conversion
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 Modeling expert opinion in ModelRisk
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 Expert
 ModelRisk introduction
 Building and running a simple example model
 Distributions in ModelRisk
 List of all ModelRisk functions
 Custom applications and macros
 ModelRisk functions explained
 Tamara  project risk analysis
 Introduction to Tamara project risk analysis software
 Launching Tamara
 Importing a schedule
 Assigning uncertainty to the amount of work in the project
 Assigning uncertainty to productivity levels in the project
 Adding risk events to the project schedule
 Adding cost uncertainty to the project schedule
 Saving the Tamara model
 Running a Monte Carlo simulation in Tamara
 Reviewing the simulation results in Tamara
 Using Tamara results for cost and financial risk analysis
 Creating, updating and distributing a Tamara report
 Tips for creating a schedule model suitable for Monte Carlo simulation
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 Correlation with project schedule risk analysis
 Pelican  enterprise risk management
 ModelRisk Cloud system
 ModelRisk Cloud introduction
 Getting your software ready
 Starting ModelRisk Cloud
 Uploading a risk analysis model
 Creating a new scenario for the risk analysis model
 Running a Monte Carlo simulation of the model
 Uploading a SID (Simulation Imported Data file)
 Building a risk analysis model that uses SIDs
 Viewing the Monte Carlo results from a simulation run
 Administrator's use of ModelRisk Cloud
 Preparing a risk analysis model for upload to ModelRisk Cloud
 ModelRisk Result Viewer
Enterprise Risk Management software (ERM)
Learn more about our enterprise risk analysis management software tool, Pelican