Experimental. This function is part of the experimental probabilistic network module and the API may change in future versions.
Arguments
- network
A prob_net object created by
prob_net().- num_samples
Number of samples to simulate for each node (default is 1000).
Details
This function performs inference on a probabilistic network of project risks by simulating random samples from the distribution of each node. The function supports normal, uniform, lognormal, discrete, conditional distributions, and aggregate nodes that sum the values of specified continuous nodes.
Aggregate nodes are computed as the sum of values from the specified continuous nodes.
Conditional nodes depend on a discrete conditional node; if the condition is true (value = 1),
the node follows the true_dist, otherwise it follows the false_dist (value = 0).
For discrete distributions, sampling is performed using sample().
Examples
# Define nodes
nodes <- data.frame(
id = c("A", "B", "C", "D"),
label = c("Node A", "Node B", "Node C", "Node D"),
stringsAsFactors = FALSE
)
# Define links
links <- data.frame(
source = c("A", "A", "B", "C"),
target = c("B", "C", "D", "D"),
weight = c(1, 2, 3, 4),
stringsAsFactors = FALSE
)
# Define distributions for nodes
distributions <- list(
A = list(type = "discrete", values = c(0, 1), probs = c(0.5, 0.5)),
B = list(type = "normal", mean = 2, sd = 0.5),
C = list(
type = "conditional", condition = "A",
true_dist = list(type = "normal", mean = 1, sd = 0.5),
false_dist = list(type = "lognormal", meanlog = 0, sdlog = 0.2)
),
D = list(type = "aggregate", nodes = c("B", "C"))
)
# Create the network graph
graph <- prob_net(nodes, links, distributions = distributions)
# Perform inference (simulate 1000 samples)
simulation_results <- prob_net_sim(graph, num_samples = 1000)
head(simulation_results)
#> A B C D
#> 1 1 2.500190 1.4038386 3.904028
#> 2 0 1.712608 0.9651617 2.677770
#> 3 0 1.819046 0.7255989 2.544645
#> 4 1 2.849409 0.6168564 3.466266
#> 5 0 2.206482 1.0417206 3.248202
#> 6 0 2.344146 1.0655631 3.409709