C. Credibility  

• Apply limited fluctuation (classical) credibility including criteria for both full and partial credibility.
  
  
• Perform Bayesian analysis using both discrete and continuous models.
  
  
• Apply Bühlmann and Bühlmann-Straub models and understand the relationship of these to the Bayesian model.
  
  
• Apply conjugate priors in Bayesian analysis and in particular the Poisson-gamma model.
  
  
• Apply empirical Bayesian methods in the nonparametric and semiparametric cases.  

D. Interpolation and Smoothing  

• Demonstrate an understanding of the purpose of smoothing data.
     
• Apply polynomial splines, and cubic splines in particular to actuarial data.

E. Simulation  

• Simulate both discrete and continuous random variables using the inversion method.
      
• Estimate the number of simulations needed to obtain an estimate with a given error and a given degree of confidence.
  
  
• Use simulation to determine the p-value for a hypothesis test.
     
• Use the bootstrap method to estimate the mean squared error of an estimator.
  
  
• Apply simulation methods within the context of actuarial models.

Texts

• Loss Models: From Data to Decisions, (Second Edition), 2004, by Klugman, S.A., Panjer, H.H. and Willmot, G.E., Chapter 1, Section 1.1 only, Chapters 9–11, Chapter 12 (excluding 12.5.4, 12.5.5 and 12.6), Chapter 13, Chapter 15 and Chapter 17.

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