Bayesian inference for calculating random samples of a cost given risk event(s).
Arguments
- num_sims
Number of random samples to draw from the mixture model.
- risk_probs
A vector of probabilities for each risk event 'R_i'.
- means_given_risks
A vector of means of the normal distribution for cost 'A' given each risk event 'R_i'.
- sds_given_risks
A vector of standard deviations of the normal distribution for cost 'A' given each risk event 'R_i'.
- base_cost
The baseline cost given no risk event occurs.
Examples
# Example with three risk events
num_sims <- 1000
risk_probs <- c(0.3, 0.5, 0.2)
means_given_risks <- c(10000, 15000, 5000)
sds_given_risks <- c(2000, 1000, 1000)
base_cost <- 2000
samples <- cost_pdf(
num_sims = num_sims,
risk_probs = risk_probs,
means_given_risks = means_given_risks,
sds_given_risks = sds_given_risks,
base_cost = base_cost
)
hist(samples, breaks = 30, col = "skyblue", main = "Histogram of Cost", xlab = "Cost")
