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quantify()-ing probabilities

With prob = TRUE, quantify() treats each column as a failure probability for that basic event and returns the top-event probability for each row (assuming independence in the fast implementation).

This vignette shows:

  1. Turning binary scenario tables into probability tables with populate().
  2. Projecting probabilities over time from hourly rates (IT security example).

Database example: populate() then quantify()

db_probs is in long form (event, probability). populate() replaces 1s in a binary scenario with those probabilities (0 stays 0):

library(tidyfault)
library(dplyr)
library(ggplot2)
library(tidyr)
library(tibble)

data("db_nodes")
data("db_edges")
data("db_outcomes_binary")
data("db_probs")

f <- curate(db_nodes, db_edges) |>
  equate() |>
  formulate()

db_outcomes_probs <- populate(db_outcomes_binary, db_probs)
knitr::kable(head(db_outcomes_probs, 6), digits = 3)
scenario DC AF SF BF NF AUF HF MF
1 0.00 0.1 0.00 0.15 0.00 0.00 0.00 0.2
2 0.00 0.0 0.08 0.00 0.12 0.00 0.00 0.2
3 0.05 0.0 0.00 0.15 0.00 0.00 0.00 0.0
4 0.00 0.1 0.00 0.00 0.12 0.00 0.04 0.0
5 0.00 0.0 0.00 0.15 0.00 0.06 0.00 0.2
6 0.05 0.0 0.00 0.00 0.12 0.00 0.00 0.2

Then pass the probability matrix to quantify():

prob_top <- quantify(f, newdata = db_outcomes_probs, prob = TRUE)
prob_top
#>  [1] 0.100 0.120 0.050 0.208 0.060 0.164 0.100 0.000 0.208 0.107
summary(prob_top)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>  0.0000  0.0700  0.1035  0.1117  0.1530  0.2080

IT security: rates to cumulative probabilities over time

The next chunk defines illustrative constant failure rates per hour (same orders of magnitude as the JSS article). For each mission time t, map rates to cumulative failure probabilities with the exponential distribution \(P(T \le t) = 1 - e^{-\lambda t}\):

data("it_security_nodes")
data("it_security_edges")

f_sec <- formulate(equate(curate(it_security_nodes, it_security_edges)))

# Illustrative constant failure rates (per hour), as in the paper source
it_security_rates <- tibble::tibble(
  DA = 0.25 / 24 / 365,
  EP = 0.20 / 24 / 365,
  IM = 0.09 / 24 / 365,
  LR = 0.13 / 24 / 365,
  MN = 0.29 / 24 / 365,
  PO = 0.16 / 24 / 365,
  PC = 0.22 / 24 / 365,
  PM = 0.15 / 24 / 365,
  VS = 0.11 / 24 / 365,
  WB = 0.17 / 24 / 365
)
rate_names <- names(it_security_rates)

t_hours <- seq(0, 50000, by = 1000)
it_security_projections <- crossing(t = t_hours, it_security_rates) |>
  mutate(across(all_of(rate_names), ~ pexp(q = t, rate = .x)))

it_security_projections$outcome <- quantify(
  f_sec,
  newdata = it_security_projections[, rate_names, drop = FALSE],
  prob = TRUE
)

it_security_projections |>
  select(t, outcome) |>
  head(6)
#> # A tibble: 6 × 2
#>       t   outcome
#>   <dbl>     <dbl>
#> 1     0 0        
#> 2  1000 0.0000325
#> 3  2000 0.000250 
#> 4  3000 0.000811 
#> 5  4000 0.00185  
#> 6  5000 0.00347

Plot top-event probability versus time:

ggplot(it_security_projections, aes(x = t, y = outcome)) +
  geom_line() +
  labs(
    x = "Time (hours)",
    y = "Top-event probability"
  )

Summary

Task Approach
Scenario-specific event probabilities populate() from binary + long event / probability table
Direct probability scenarios Data frame of probabilities + quantify(..., prob = TRUE)
Time profiles Build rows of \(P(\text{event})\) at each \(t\), then same call

Uncertainty simulation is covered in Simulating uncertainty.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.