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Ms Rashmika Singh graduated summa cum laude with her BSc Honours in Statistics, along the way receiving six certificates of merits from her seven modules and the highest mark for her Honours project. The project considered two primary methods of analysis, which were the Kaplan-Meier method and the Cox Proportional Hazards method. This year Singh is pursuing her Masters in Statistics in the area of Survival Analysis with particular application to HIV, jointly supervised by Ramjith and Professor Glenda Matthews.
Singh described the 2012 year as was ‘one of the most fulfilling years of my academic journey thus far’.
Q-Q Research staff is experienced in entering large amounts of data, conducting data analysis, and participating in ongoing research and data collection for federally funded research projects.
Q-Q Research Consultants is a full service consulting firm with extensive experience in research and program evaluation. In part 1 we saw that network graphs provide invaluable information in terms of understanding story development, character importance, and “character death” prediction with limited external input. In this post, we use classical survival modelling to measure the relative risk of dying faced by each GoT character over the course of the story, and determine which events significantly impact the survivability of characters. Consequently, we have to take care not to “overfit” the data and design features that are as generic as possible.
A predictive model is only as good as its features and a book series that complex offers plenty to choose from, which makes the selection step difficult.
With the timeline, we calculate an Andersen-Gill proportional hazard model[4] to measure the impact the presence (or absence) our selected features have on a character’s chance of survival.
The baseline survival curve (survival of an average character) associated with the model shows that ~ 20% of characters are expected to die over the course of 5 books. We measure the predictive accuracy of the proportional hazard model by comparing its performance to the graph-based belief propagation of part 1.
Above: Here again is a strong agreement between graph and event models, with Renly showing a risk twice as high as anyone else and fourteen times higher than an average character.
Above: From the red wedding onwards, we see that the survival and the belief propagation models start to differ.
Above: Prior to that point, a couple of characters always showed a distinctly heightened death risk.
One advantage of the proportional hazard model however is being able to model who lives on.
Considering the number of times Jaime, Arya and Tyrion have been captured and they are in the “backseat” in most proceedings, they are practically immortal in our model. The different models’ output can be combined for greater accuracy, and there are an almost infinite number of ways to do so, ranging from the trivial “Take one at random” to “Gradient boosted regression trees”.
We keep combining simple by adding up, for each character, the number of standard deviation their prediction deviates from the model’s average[8]. Above: Because the social network graph at the end of book 5 is very dispersed, variations between characters are small, and so the output of the survival model is more significant. From a strict numbers perspective, the event-based model leads to more accurate predictions. The belief propagation model of part 1 is very accurate when the available ground truth is reliable and the structure of the network is dense.


As shown by the divergent predictions for the red wedding, both models provide complementary information that is unavailable to the other (social structure versus events). The increased complexity of the story is well captured by the evolution of the social networks over time. Clement is a data scientist with Teradata Australia and New Zealand Advance Analytics group. Use case diagram in the Unified Modeling Language (UML) is a type of behavioral diagram defined by and created from a Use-case analysis. Her dissertation titled ‘Modeling Survival Data with an Application to Criminal Recidivism’, was supervised by Mr Jordache Ramjith.
Singh’s project was subsequently entered into the annual South African Statistics Association (SASA) National Project competition, the results of which are expected to be released in June 2013. Stories are arguably more likely to be written according to a small set of rules rather than a strict adherence to graph theory.
Practically, one could easily design a small set of features that would retrospectively predict character deaths exactly [1]. We create time-specific data by recording each event occurrence with respect to its page number to create an event timeline[3].
In fact, the proportional hazard model shows that most selected events are good individual predictors.
The almost 100% survival rates in the first few hundred pages correspond to the setup period of the book series where very few people die. To do so, we calculate the relative risk of death for characters at the same points in time[5].
Here, Robb is correctly modeled as the character most likely to die, but Catelyn Stark is further away. While the predictions overlap at times, they provide a different view of the current death likelihoods as they rely on different information: social interactions for graphs versus events for the survival model. However, it requires a thorough understanding of the data, and significant effort to extract relevant events from the books. On the plus side, graphs require very limited knowledge of the subject matter, no “hindsight” information and provide information beyond predictive abilities such as seeing how the story evolves or measuring character importance. Concluding the saga will require the graph to become more compact again, assuming the books reach a single conclusion. Given that it took 5 books to expand the network and that only 2 more are planned, expect some savagery in the coming weeks, if only to conclude the story.
With a background in Color Science, Computational Photography and Computer Vision, Clement has designed and build perceptual statistical experiments and models for the past 10 years.
Its purpose is to present a graphical overview of the functionality provided by a system in terms of actors, their goals (represented as use cases), and any dependencies between those use cases.The main purpose of a use case diagram is to show what system functions are performed for which actor. The views expressed on this website are his own and do not necessarily reflect the views of his former, current or future employers. From 2008 to 2012 she was funded by Investec Bank and in addition received funding from the National Research Foundation.
Only hard work, dedication and support helped me to achieve the successful completion of my Honours degree,’ she said. Additionally, staff has extensive experience in creating and maintaining large data sets, including merging data from various sources, conducting data integrity checks to limit data issues or anomalies, and compiling data to analyze, and generate reports and graphs.
This follows from the model characteristics; being both a King and a man radically increases Robb Stark’s death risk. In the books, he definitely dies at some point after the Red Wedding, but he is said to have been resuscitated a few times beforehand.


Stannis is the likeliest to die[6], closely followed by the Greyjoy brothers[7] who have had a very limited impact on the story (so far?). Clement strives to combine his psychometric, perceptual and statistical knowledge to deliver insights and their story that are understandable and actionable to non-technical audiences. Sanjeev also blogs about technology, travel and other things that he is passionate about on his blog. Staff has computed means, frequencies, cross tabulations, odds ratios, ANOVAs and regressions using these data. This illustrates one of the challenges of attempting analytics on a fantasy book: there are limits to certainty. After that comes a gaggle of characters with above average risk (including Jon Snow); similar to the LBP model. If this interaction is essential to a coherent description of the desired behavior, perhaps the system or use case boundaries should be re-examined.
Moreover, they have an understanding of the appropriate handling of missing data using the multiple imputation expectation maximization strategies, as well as outlier robust analytic techniques. Nevertheless, Joffrey shows a very high death risk; the Clegane brothers are not too far off. This is useful for extracting truly common behaviors from multiple use cases into a single description.
The notation is a dashed arrow from the including to the included use case, with the label "«include»". This usage resembles a macro expansion where the included use case behavior is placed inline in the base use case behavior. To specify the location in a flow of events in which the base use case includes the behavior of another, you simply write include followed by the name of use case you want to include, as in the following flow for track order.ExtendIn another form of interaction, a given use case (the extension) may extend another. This relationship indicates that the behavior of the extension use case may be inserted in the extended use case under some conditions. The notation is a dashed arrow from the extension to the extended use case, with the label "«extend»". The notes or constraints may be associated with this relationship to illustrate the conditions under which this behavior will be executed.Modelers use the «extend» relationship to indicate use cases that are "optional" to the base use case.
A given use case may have common behaviors, requirements, constraints, and assumptions with a more general use case. In this case, describe them once, and deal with it in the same way, describing any differences in the specialized cases. The notation is a solid line ending in a hollow triangle drawn from the specialized to the more general use case (following the standard generalization notation)AssociationsAssociations between actors and use cases are indicated in use case diagrams by solid lines. An association exists whenever an actor is involved with an interaction described by a use case. Associations are modeled as lines connecting use cases and actors to one another, with an optional arrowhead on one end of the line. The arrowhead is often used to indicating the direction of the initial invocation of the relationship or to indicate the primary actor within the use case.



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