Description: An exploration of the seasonality of mortality and how total population mortality is related to flu and pneumonia mortality.

Description: It is about using actuarial judgement to validate the predictive model.

Description: Describes goals of the section for the coming year.

Description: Evaluates the ability of various model types to automatically approximate various mathematical formulas. This can be used as a guide for feature engineering.

Description: Reflections on SOA delphi study released Oct 2005

Description: Spark offers a way to get some dramatic speed and scalability advantages over the Map-reduce methodology whenever you are doing iterative data processing and particularly when you want interactive data analysis capability. Find out why “lazy execution” can be a desirable characteristic when dealing with big data.

Description: Can the aggregate results from a group be used to forecast something as subjective and judgmental as one single person’s opinion? Forecasting of this type commonly uses a set of techniques known as collaborative filtering which is discussed at length in this article.

Description: Given the ubiquity of judgmental forecasting, it is important to understand the potential biases inherent in human judgment—the traps that can snare us—and to learn methods and best practices to avoid them. The aim of this article is to help readers with these goals.

Description: The author discusses the two-part presentation on complexity science tools at the SOA Annual Meeting October 2010. He explains that presentations went back to basics with explanations about the difference between modeling tools used to employ deterministic methods for classical actuarial models.

Description: This article discussed the NEAT family of neural network algorithms. NEAT automatically determines the number of hidden neurons, as well as their structure. Additionally HyperNEAT can scale to very large problem domains.