Perform Bayesian Learning on a Probabilistic Network of Project Risks.

This function updates a probabilistic network of project risks with observed values for certain nodes and then performs inference to generate posterior distributions for unobserved nodes. The function supports normal, uniform, lognormal, conditional continuous, conditional discrete, discrete, and aggregate (summation) node types.

Usage

prob_net_learn(network, observations = list(), num_samples = 1000)

Arguments

network: A prob_net object created by `prob_net()`.
observations: A named list where names are node IDs and values are observed values.
num_samples: Number of samples to simulate for each node (default is 1000).

Value

A data frame with `num_samples` rows and one column per node containing the simulated posterior samples.

Details

Normal nodes are sampled from a normal distribution using the specified mean and sd. Uniform nodes are sampled from a uniform distribution between the specified min and max values. Lognormal nodes are sampled from a lognormal distribution with specified meanlog and sdlog. 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). Conditional distributions can be normal, lognormal, uniform, or discrete. Discrete nodes are sampled using `sample()`, and aggregate nodes are computed as the sum of values from the specified nodes. Observed nodes are fixed at their given values.

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 = "discrete", values = c(0, 1), probs = c(0.4, 0.6))),
  D = list(type = "aggregate", nodes = c("B", "C"))
)

# Create the network graph
graph <- prob_net(nodes, links, distributions = distributions)

# Perform Bayesian updating with observations
observations <- list(A = 1)
updated_results <- prob_net_learn(graph, observations, num_samples = 1000)
head(updated_results)
#>   A        B         C        D
#> 1 1 2.902341 0.2902422 3.192583
#> 2 1 2.226350 0.7261965 2.952547
#> 3 1 1.786184 1.1850027 2.971187
#> 4 1 2.240081 0.4780507 2.718131
#> 5 1 1.353897 0.6481988 2.002096
#> 6 1 2.083298 0.6129808 2.696279