Split Triangle distribution
SplitTriangle(low,medium,high,lowP,mediumP,highP)
Uses
The SplitTriangle is used to model expert opinion where the SME is asked for three points on the distribution: for each point the SME provides a value of the variable and their view of the probability of being less than that value. The SplitTriangle then extrapolates from these values to create a distribution composed of 2 triangles as in the figure above.
ModelRisk functions added to Microsoft Excel for the Split-Triangle distribution
VoseSplitTriangle generates random values from this distribution for Monte Carlo simulation, or calculates a percentile if used with a U parameter.
VoseSplitTriangleObject constructs a distribution object for this distribution.
VoseSplitTriangleProb returns the probability density or cumulative distribution function for this distribution.
VoseSplitTriangleProb10 returns the log10 of the probability density or cumulative distribution function.
Split Triangle distribution equations
ModelRisk
Monte Carlo simulation in Excel. Learn more
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Adding risk and uncertainty to your project schedule. Learn more
Navigation
- Risk management
- Risk management introduction
- What are risks and opportunities?
- Planning a risk analysis
- Clearly stating risk management questions
- Evaluating risk management options
- Introduction to risk analysis
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- Approximations to the Inverse Hypergeometric Distribution
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- Stirlings formula for factorials
- Normal approximation to the Beta Distribution
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- Normal_approximation_to_the_Binomial_distribution
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- Correlation modeling in risk analysis
- Common mistakes when adapting spreadsheet models for risk analysis
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- ModelRisk's Six Sigma functions
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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 introduction
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- Kolmogorov-Smirnoff (K-S) Statistic
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- Goodness of fit statistics
- The Chi-Squared Goodness-of-Fit Statistic
- Determining the joint uncertainty distribution for parameters of a distribution
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- Does a parametric distribution exist that is well known to fit this type of variable?
- Censored data
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- Information criteria - SIC HQIC and AIC
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- Does the random variable follow a stochastic process with a well-known model?
- Fitting a distribution for a discrete variable
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- Fitting a first order parametric distribution to observed data
- Fitting a discrete non-parametric first-order 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
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- Bootstrap
- The Bootstrap
- Linear regression parametric Bootstrap
- The Jackknife
- Multiple variables Bootstrap Example 2: Difference between two population means
- Linear regression non-parametric 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 non-parametric 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
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- Prior distributions
- Bayesian analysis with threshold data
- Bayesian analysis example: gender of a random sample of people
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- Hyperparameters
- Hyperparameter example: Micro-fractures 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
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- Bayesian analysis example: The Monty Hall problem
- Determining prior distributions for uncorrelated parameters
- Subjective priors
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- Bayesian analysis example: identifying a weighted coin
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- Likelihood functions
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- 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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- More on Conversion
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- Modeling expert opinion in ModelRisk
- Multivariate Time Series
- Sum Product
- Aggregate DePril
- Aggregate Discrete
- 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
- Random number generator and sampling algorithms used in Tamara
- Probability distributions used in Tamara
- 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
