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ModelRisk Basic Intro
In this webinar David Vose shows the features of ModelRisk Standard, now rebranded to ModelRisk Basic. This one hour free webinar is ideal if you just downloaded ModelRisk and want to learn more about the capabilities of this powerfull software tool.
This is a recording of a live webinar. We have not included the live question round in this recording. In the webinar David Vose mentions that the Standard edition is free. However, that offer has now expired.
ModelRisk Complete Intro (1)
In this webinar David Vose shows the features of ModelRisk Professional, now rebranded to ModelRisk Complete. This one hour free webinar is ideal if you want to learn more about the advanced capabilities of ModelRisk software. Part 1 of 2.
ModelRisk Complete Intro (2)
In this webinar David Vose shows the features of ModelRisk Professional, now rebranded to ModelRisk Complete. This one hour free webinar is ideal if you want to learn more about the advanced capabilities of ModelRisk software. Part 2 of 2.
Modeling extremes
Assessing the size and probability of extreme events is central to many fields, from finance and insurance, to epidemiology, utility demand, weather events, and engineering design. ModelRisk has some very powerful, yet simple and flexible, tools to allow you to quickly evaluate extremes probabilistically.
Management consultants, investment advisers, corporate financial analysts, traders, insurance analysts, engineers, ...
Risk Modeling for Basel II
Central to the principles of Basel II (and III) is the necessity to model as precisely as possible a bank’s level of financial exposure. The more precisely a bank can do this, the less capital it needs to set aside and the greater its profitability. ModelRisk has a wide range of risk modeling tools specifically designed for financial industries, which will be demonstrated in this short webinar.
Insurance and Finance
An introduction to the PK/PD module
PK/PD (Pharmacokinetic/Pharmacodynamic) modeling is a technique that combines the pharmacologic disciplines of pharmacokinetics and pharmacodynamics. ModelRisk has a special PK/PD module that allows the incorporation of parameter uncertainty and individual variability into such models. This short demo will show you how it works and the benefits it brings.
Pharmacy
Correlation in risk modeling
Correlation is a critical component of a risk analysis model in any field. Failure to correctly model the correlation between uncertain variables will give misleading, and sometimes dangerously wrong, results yet it receives very little attention. Most risk analysis software products provide only the most basic correlation tools. ModelRisk is very different. This short demo explains the correlation tools available in ModelRisk, how they work, and why they can make all the difference to your risk analysis.
Linking ModelRisk to databases
ModelRisk has the capability to connect to a wide variety of databases, allowing you to build risk analysis models that use the most up-to-date data automatically. This short demo explains how the database tools work, and how you can build concise, adaptable models that always stay current.
Splicing distributions
It is very common in many fields of risk analysis to fit distributions to data. With well over 100 distributions available in ModelRisk, you will often be able to choose a distribution that fits your data well. The most common problem in fitting to data is that the bulk of the data fit nicely, but the level of fit is poor in the high-end tail. ModelRisk has a tool that allows you to ‘splice’ two distributions together and fit the combination to your data, ensuring that you get a good representation of the entire range of possible outcomes. This short demo shows how this tool works.
Tolerance analysis
Karl Luce documents the important role Monte Carlo Analysis (MCA) provides for Tolerance Analysis. Using the example of an overrunning one-way clutch, a paper and associated MS Excel Workbook Karl introduces us into the world of Tolerance Analysis. Content:
Advanced Time Series Forecasting
This webinar will illustrate how to review historic data and use your knowledge of the system you are modeling to create more robust, credible forecasts that incorporate appropriate levels of uncertainty.
The webinar explains how to go about reviewing your data and the statistical tools and techniques that you can help reveal the underlying patterns. We will focus most on the thinking that underpins time series forecasting, using three different examples as illustration. Areas covered include: