CFI

Question 1

Monte carlo in 4 steps

Answer 1

Past observations + probability distribution + simulations + quantify the range of scenarios (CI)

Question 2

Law of large numbers

Answer 2

As the sample size grows, the observed probability approaches the theoretical probability.

Question 3

Monte carlo & why you need a probabliity distribution

Answer 3

The probability distribution lets you calculate the mean, standard deviation and other metrics that describe past behavior

Question 4

Binomial distribution

Answer 4

The probability of a binary outcome (yes or no)

Question 5

Poisson distribution

Answer 5

Happens in discrete events for modeling how many times an event would happen in a given time period

Question 6

Beta distribution

Answer 6

Best used when we have limited data to form a probability (e.g., predict a student’s GPA with limited data)

Question 7

Gamma distribution

Answer 7

Used for positively skewed continuous values (e.g., the probability that a bank teller gets more than 20 customers within an hour)

Question 8

Log distribution

Answer 8

Commonly applied with a relatively small mean with large variances (e.g., expected life of machinery)

Question 9

Use cases for Monte Carlo

Answer 9

stock price, assess the probability of deal or no deal in a M&A, cash flow analysis (capture the variability of cash flows to plan for unforeseen events)