Exam STAM - The Infinite Actuary

A.1 Probability Review ... lessons ... min of video

A.1.1 Describing Distributions ...
- A.1.1 #1 (1:43)
- A.1.1 #2 (3:04)
- A.1.1 #3 (1:03)
- A.1.1 #4 (1:24)
A.1.2 Moments ...
- A.1.2 #1 (7:30)
- A.1.2 #2 (2:24)
- A.1.2 #3 (2:33)
- A.1.2 #4 (2:19)
- A.1.2 #5 (3:38)
- A.1.2 #6 (3:16)
- A.1.2 #7 (6:03)
- A.1.2 #8 (2:26)
- A.1.2 #9 (1:53)
A.1.3 Generating Functions ...
- A.1.3 #1 (4:35)
- A.1.3 #2 (4:06)
- A.1.3 #3 (3:50)
- A.1.3 #4 (1:03)
- A.1.3 #5 (3:20)
- A.1.3 #6 (2:17)
A.1.4 Joint and Conditional Distributions ...
- A.1.4 #1 (0:28)
- A.1.4 #2 (1:57)
A.1.5 Conditional Moments ...
- A.1.5 #1 (2:28)
- A.1.5 #2 (2:36)
- A.1.5 #3 (2:16)
A.1.6 Mixtures ...
- A.1.6 #1 (3:19)
- A.1.6 #2 (3:11)
- A.1.6 #3 (2:46)
- A.1.6 #4 (1:02)
A.1.7 Sums of Random Variables ...
- A.1.7 #1 (1:59)
- A.1.7 #2 (1:00)
- A.1.7 #3 (4:53)

A.2 Key Continuous Distributions ... lessons ... min of video

A.2.1 The Exponential Distribution ...
- A.2.1 #1 (2:04)
- A.2.1 #2 (2:05)
- A.2.1 #3 (4:25)
- A.2.1 #4 (6:10)
- A.2.1 #5 (3:35)
- A.2.1 #6 (2:17)
- A.2.1 #7 (1:07)
- A.2.1 #8 (4:24)
A.2.2 The Gamma Distribution ...
- A.2.2 #1 (2:07)
- A.2.2 #2 (2:54)
A.2.3 The Pareto Distribution ...
- A.2.3 #1 (2:12)
- A.2.3 #2 (4:09)
- A.2.3 #3 (2:08)
- A.2.3 #4 (1:14)
- A.2.3 #5 (0:59)
- A.2.3 #6 (3:36)
A.2.4 Normal and Lognormal Distributions ...
- A.2.4 #1 (3:41)
- A.2.4 #2 (2:04)
- A.2.4 #3 (2:19)
- A.2.4 #4 (3:33)
- A.2.4 #5 (1:44)
- A.2.4 #6 (3:59)
- A.2.4 #7 (3:15)
- A.2.4 #8 (3:55)
- A.2.4 #9 (3:47)
- A.2.4 #10 (3:46)
- A.2.4 #11 (3:02)
- A.2.4 #12 (2:54)
- A.2.4 #13 (1:03)
- A.2.4 #14 (3:19)
- A.2.4 #15 (3:20)
A.2.5 Uniforms and Summary ...
- A.2.5 #2 (0:44)
- A.2.5 #3 (2:11)

A.3 Severity Models ... lessons ... min of video

A.3.1 Scale Parameters ...
- A.3.1 #1 (3:29)
- A.3.1 #2 (2:21)
- A.3.1 #3 (0:39)
- A.3.1 #4 (2:05)
- A.3.1 #5 (2:00)
- A.3.1 #10 (3:28)
A.3.2 Deductibles ...
- A.3.2 #1 (2:16)
- A.3.2 #2 (1:53)
- A.3.2 #3 (2:16)
- A.3.2 #4 (2:38)
- A.3.2 #5 (1:17)
- A.3.2 #6 (1:09)
- A.3.2 #7 (2:19)
- A.3.2 #8 (5:36)
A.3.3 Limits ...
- A.3.3 #1 (4:43)
- A.3.3 #2 (3:57)
- A.3.3 #3 (3:07)
- A.3.3 #4 (3:07)
- A.3.3 #5 (3:09)
A.3.4 Other Adjustments ...
- A.3.4 #1 (1:14)
- A.3.4 #2 (5:20)
- A.3.4 #3 (2:09)
- A.3.4 #4 (2:27)
- A.3.4 #5 (3:01)
- A.3.4 #6 (1:55)
- A.3.4 #7 (1:40)
A.3.5 Variances ...
- A.3.5 #1 (1:18)
- A.3.5 #2 (3:13)
- A.3.5 #3 (6:17)
- A.3.5 #4 (6:31)
- A.3.5 #5 (3:06)
A.3.6 Decomposing Losses (Optional) ...
- A.3.6 #1 (1:39)
- A.3.6 #2 (1:47)
- A.3.6 #3 (3:08)

A.4 Misc. Continuous Topics ... lessons ... min of video

A.4.1 Transformations ...
- A.4.1 #1 (4:40)
- A.4.1 #2 (0:59)
- A.4.1 #3 (2:04)
- A.4.1 #4 (1:59)
- A.4.1 #5 (1:54)
A.4.2 Spliced Distributions ...
- A.4.2 #1 (1:36)
- A.4.2 #2 (3:08)
- A.4.2 #3 (4:53)
- A.4.2 #4 (4:18)
- A.4.2 #5 (7:06)
- A.4.2 #6 (5:28)
- A.4.2 #7 (2:15)
- A.4.2 #8 (2:59)
A.4.3 Frailty Models ...
- A.4.3 #1 (3:08)
- A.4.3 #2 (2:29)
- A.4.3 #3 (4:09)
- A.4.3 #4 (2:59)
- A.4.3 #5 (2:49)
- A.4.3 #6 (2:55)
- A.4.3 #7 (2:59)
A.4.4 Tail Weights ...
- A.4.4 #1 (4:38)
- A.4.4 #2 (1:06)

A.5 Frequency Models ... lessons ... min of video

A.5.1 Key Discrete Distributions ...
- A.5.1 #3 (2:17)
- A.5.1 #4 (4:32)
- A.5.1 #5 (1:36)
- A.5.1 #6 (1:43)
- A.5.1 #7 (0:47)
- A.5.1 #8 (1:46)
- A.5.1 #9 (2:45)
- A.5.1 #10 (1:20)
- A.5.1 #11 (3:31)
- A.5.1 #12 (1:24)
A.5.2 Poisson-Gamma ...
- A.5.2 #1 (1:56)
- A.5.2 #2 (0:57)
- A.5.2 #3 (1:53)
- A.5.2 #4 (2:19)
A.5.3 (a, b, 0) Definition ...
- A.5.3 #1 (1:41)
- A.5.3 #2 (2:30)
- A.5.3 #3 (0:59)
A.5.4 (a, b, 1) Distributions ...
- A.5.4 #1 (0:43)
- A.5.4 #2 (2:21)
- A.5.4 #3 (3:01)
A.5.5 Moments of (a, b, 1) Distributions ...
- A.5.5 #1 (1:01)
- A.5.5 #2 (3:10)

A.6 Aggregate Models ... lessons ... min of video

A.6.1 Normal Approximations ...
- A.6.1 #3 (2:56)
- A.6.1 #5 (2:30)
- A.6.1 #10 (2:53)
- A.6.1 #11 (3:16)
- A.6.1 #12 (2:31)
- A.6.1 #13 (4:10)
- A.6.1 #14 (0:41)
- A.6.1 #17 (0:51)
- A.6.1 #18 (2:05)
- A.6.1 #27 (4:35)
- A.6.1 #28 (3:55)
A.6.2 Discrete Severities ...
- A.6.2 #2 (5:41)
- A.6.2 #3 (1:25)
- A.6.2 #4 (3:14)
A.6.3 Deductibles Per Loss ...
- A.6.3 #1 (1:16)
- A.6.3 #2 (1:34)
- A.6.3 #3 (1:14)
- A.6.3 #4 (1:18)
A.6.4 Aggregate Deductibles ...
- A.6.4 #1 (5:34)
- A.6.4 #2 (6:56)
- A.6.4 #3 (1:06)
- A.6.4 #4 (1:31)
A.6.5 Continuous Aggregate Distributions ...
- A.6.5 #1 (1:49)
- A.6.5 #2 (1:59)

B.1 VaR and TVaR ... lessons ... min of video

B.1.1 Risk Measures ...
- B.1.1 #1 (2:11)
- B.1.1 #2 (1:32)
- B.1.1 #3 (2:40)
- B.1.1 #4 (1:33)
- B.1.1 #6 (1:38)
- B.1.1 #7 (1:02)
- B.1.1 #8 (3:02)
B.1.2 Normal and Lognormal Distributions ...
- B.1.2 #1 (2:19)
- B.1.2 #2 (2:02)
- B.1.2 #3 (2:38)
- B.1.2 #4 (2:54)
- B.1.2 #5 (3:45)
B.1.3 Deductibles ...
- B.1.3 #1 (2:12)
- B.1.3 #2 (3:12)
- B.1.3 #3 (1:58)
- B.1.3 #4 (8:56)

B.2 Coherence ... lessons ... min of video

B.2.1 Coherence ...
- B.2.1 #1 (4:59)
- B.2.1 #2 (0:31)
- B.2.1 #3 (0:41)
- B.2.1 #4 (3:38)

C.1 Maximum Likelihood Estimators ... lessons ... min of video

C.1.1 Maximum Likelihood Estimators ...
- C.1.1 #1 (2:37)
- C.1.1 #2 (1:32)
- C.1.1 #3 (4:01)
- C.1.1 #4 (1:44)
- C.1.1 #5 (7:11)
- C.1.1 #6 (1:53)
- C.1.1 #7 (1:39)
- C.1.1 #8 (1:42)
- C.1.1 #9 (1:40)
- C.1.1 #10 (2:43)
- C.1.1 #11 (3:11)
- C.1.1 #12 (1:53)
- C.1.1 #13 (2:44)
- C.1.1 #14 (4:04)
- C.1.1 #15 (1:57)
- C.1.1 #16 (1:09)
C.1.2 Special Cases ...
- C.1.2 #1 (1:39)
C.1.3 Exponentials ...
- C.1.3 #1 (1:03)
- C.1.3 #2 (4:16)
- C.1.3 #3 (1:16)
- C.1.3 #4 (3:50)
C.1.4 Range Depends on Parameters ...
- C.1.4 #1 (1:58)
C.1.5 Transformations ...
- C.1.5 #1 (1:00)
- C.1.5 #2 (1:10)
- C.1.5 #3 (2:36)
- C.1.5 #4 (1:54)
- C.1.5 #5 (3:38)
- C.1.5 #6 (2:52)
- C.1.5 #7 (1:59)

C.2 Estimating Discrete Distributions ... lessons ... min of video

C.2.1 MLE for (a, b, 1) Distributions ...
- C.2.1 #1 (0:28)
- C.2.1 #2 (1:05)
- C.2.1 #3 (5:18)
- C.2.1 #4 (5:08)
C.2.2 Which (a, b, 1) Distribution? ...
- C.2.2 #1 (2:20)
- C.2.2 #2 (2:30)
- C.2.2 #3 (1:00)

C.3 Variances of MLEs ... lessons ... min of video

C.3.1 Variances of MLEs ...
- C.3.1 #1 (1:19)
- C.3.1 #2 (1:22)
C.3.2 Fisher's Information ...
- C.3.2 #1 (0:43)
- C.3.2 #2 (2:36)
- C.3.2 #3 (4:36)
- C.3.2 #4 (2:12)
- C.3.2 #5 (3:13)
- C.3.2 #6 (2:57)
- C.3.2 #7 (1:14)
C.3.3 Fisher's Information Continued ...
- C.3.3 #1 (1:36)
C.3.4 Delta Method ...
- C.3.4 #1 (5:30)
- C.3.4 #2 (1:07)
- C.3.4 #3 (5:02)
C.3.5 Delta Method Continued ...
- C.3.5 #1 (4:45)

C.4 Hypothesis Testing ... lessons ... min of video

C.4.1 D(x) Plots ...
- C.4.1 #1 (1:41)
- C.4.1 #2 (2:18)
C.4.2 p-p plots ...
- C.4.2 #1 (0:58)
- C.4.2 #2 (0:25)
- C.4.2 #3 (1:41)
C.4.3 Hypothesis Testing ...
- C.4.3 #1 (0:43)
- C.4.3 #2 (1:50)
- C.4.3 #3 (2:44)
- C.4.3 #4 (3:29)
- C.4.3 #5 (1:42)
- C.4.3 #6 (4:10)
- C.4.3 #7 (1:37)
C.4.4 Kolmogorov-Smirnov ...
- C.4.4 #1 (4:01)
- C.4.4 #2 (2:32)
- C.4.4 #3 (2:41)
- C.4.4 #4 (3:07)
C.4.5 Chi-Square Tests I ...
- C.4.5 #1 (4:08)
- C.4.5 #2 (5:09)
- C.4.5 #3 (3:43)
- C.4.5 #4 (2:07)
- C.4.5 #5 (2:19)
- C.4.5 #6 (2:49)
C.4.7 Score Based Models ...
- C.4.7 #1 (0:42)
C.4.8 Likelihood Ratio Test ...
- C.4.8 #1 (1:22)
- C.4.8 #2 (2:13)
- C.4.8 #3 (3:21)
- C.4.8 #4 (1:21)
- C.4.8 #5 (0:35)
- C.4.8 #6 (4:36)
- C.4.8 #7 (3:08)
C.4.9 Non-Normal Confidence Intervals ...
- C.4.9 #1 (4:13)
- C.4.9 #2 (2:25)
- C.4.9 #3 (5:00)
- C.4.9 #4 (1:24)
- C.4.9 #5 (4:20)

D.1 Classical Credibility ... lessons ... min of video

D.1.1 Credibility for Frequency ...
- D.1.1 #1 (0:59)
- D.1.1 #2 (1:21)
D.1.2 Credibility for Aggregate Distributions ...
- D.1.2 #1 (1:18)
- D.1.2 #2 (3:41)
- D.1.2 #3 (1:06)
- D.1.2 #4 (1:19)
- D.1.2 #5 (4:02)
- D.1.2 #6 (2:14)
- D.1.2 #7 (1:26)
- D.1.2 #8 (1:10)
- D.1.2 #9 (2:14)
D.1.3 Partial Credibility ...
- D.1.3 #1 (1:57)
- D.1.3 #2 (3:42)
- D.1.3 #3 (2:48)
- D.1.3 #4 (1:37)

D.2 Bayesian Credibility ... lessons ... min of video

D.2.1 Discrete Bayesian Credibility ...
- D.2.1 #1 (1:18)
- D.2.1 #2 (3:51)
- D.2.1 #3 (6:03)
- D.2.1 #4 (4:28)
- D.2.1 #5 (5:01)
- D.2.1 #6 (3:15)
- D.2.1 #14 (4:02)
D.2.2 Continuous Bayesian Credibility ...
- D.2.2 #1 (2:27)
- D.2.2 #2 (4:43)
- D.2.2 #3 (1:52)
- D.2.2 #4 (6:39)
- D.2.2 #5 (3:35)
- D.2.2 #6 (2:58)
- D.2.2 #7 (3:59)
D.2.3 Poisson/Gamma ...
- D.2.3 #1 (1:23)
- D.2.3 #2 (1:51)
- D.2.3 #3 (2:38)
- D.2.3 #4 (2:47)
D.2.4 Binomial/Beta ...
- D.2.4 #1 (3:29)
- D.2.4 #2 (2:22)
- D.2.4 #3 (3:07)
- D.2.4 #4 (2:24)
D.2.6 Harder Examples ...
- D.2.6 #1 (3:19)
- D.2.6 #2 (4:00)
- D.2.6 #3 (2:41)
- D.2.6 #6 (2:54)
- D.2.6 #7 (2:35)

D.3 Buhlmann Credibility ... lessons ... min of video

D.3.1 Buhlmann Credibility ...
- D.3.1 #1 (3:01)
- D.3.1 #2 (3:56)
- D.3.1 #3 (2:13)
- D.3.1 #4 (1:56)
- D.3.1 #5 (3:29)
- D.3.1 #6 (3:28)
D.3.2 Buhlmann with Discrete Distributions ...
- D.3.2 #1 (2:52)
- D.3.2 #2 (2:45)
- D.3.2 #3 (3:09)
- D.3.2 #4 (2:33)
- D.3.2 #5 (5:00)
- D.3.2 #6 (2:30)
D.3.3 Generalizations ...
- D.3.3 #1 (4:27)
- D.3.3 #2 (1:34)
D.3.5 Buhlmann as Linear Approximation of Bayesian ...
- D.3.5 #1 (4:43)
D.3.6 Normal/Normal and Lognormal/Normal ...
- D.3.6 #1 (3:43)
- D.3.6 #2 (2:20)
- D.3.6 #3 (2:35)
- D.3.6 #4 (3:26)
- D.3.6 #5 (4:35)
- D.3.6 #6 (5:55)
- D.3.6 #7 (3:25)
- D.3.6 #8 (4:44)
- D.3.6 #9 (4:00)

D.4 Empirical Credibility ... lessons ... min of video

D.4.1 Non-Parametric Credibility: Uniform Case ...
- D.4.1 #1 (2:04)
- D.4.1 #2 (3:22)
- D.4.1 #3 (2:24)
D.4.2 Non-Parametric Credibility: General Case ...
- D.4.2 #1 (4:23)
- D.4.2 #2 (1:41)
- D.4.2 #3 (2:00)
D.4.3 Semi-Parametric Credibility: Poisson Case ...
- D.4.3 #1 (2:33)
- D.4.3 #2 (1:50)
- D.4.3 #3 (3:02)

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