README
¶
datascience Examples
This directory contains runnable Go examples demonstrating the core features of datascience.
Examples
basic_bn
Builds the classic Student Bayesian network (D, I, G, L, S), adds CPDs, validates the model, and runs a variable elimination query to compute P(G | D=0, I=1).
go run ./examples/basic_bn
structure_learning
Generates synthetic data from a known Bayesian network using forward sampling, then runs HillClimbSearch with BIC scoring to learn the structure from data. Compares learned edges against the true structure.
go run ./examples/structure_learning
causal_inference
Builds a Bayesian network with confounding and demonstrates the difference between observational conditioning P(Y | X=1) and interventional queries P(Y | do(X=1)). Computes the Average Treatment Effect (ATE).
go run ./examples/causal_inference
sampling
Builds the Student network and draws forward samples and likelihood-weighted samples with evidence. Compares empirical marginals against exact inference results.
go run ./examples/sampling
bif_io
Creates a Bayesian network, writes it to a BIF (Bayesian Interchange Format) file, reads it back, and verifies that the round-trip preserves the model structure and parameters.
go run ./examples/bif_io
Example Models
The example_models/ package provides factory functions that return fully
parameterized, well-known Bayesian networks from the literature. See
example_models/models.go for details.
Directories
¶
| Path | Synopsis |
|---|---|
|
Command basic_bn demonstrates building a Bayesian network, adding CPDs, validating the model, and running a variable elimination query.
|
Command basic_bn demonstrates building a Bayesian network, adding CPDs, validating the model, and running a variable elimination query. |
|
Command bif_io demonstrates writing a Bayesian network to BIF format and reading it back, verifying the round-trip preserves structure and CPDs.
|
Command bif_io demonstrates writing a Bayesian network to BIF format and reading it back, verifying the round-trip preserves structure and CPDs. |
|
Command causal_inference demonstrates causal reasoning with a Bayesian network, showing the difference between observational P(Y|X=1) and interventional P(Y|do(X=1)) queries, and computing the ATE.
|
Command causal_inference demonstrates causal reasoning with a Bayesian network, showing the difference between observational P(Y|X=1) and interventional P(Y|do(X=1)) queries, and computing the ATE. |
|
Package datasets provides well-known Bayesian network datasets as embedded CSV data, comparable to pgmpy's built-in datasets.
|
Package datasets provides well-known Bayesian network datasets as embedded CSV data, comparable to pgmpy's built-in datasets. |
|
Command sampling demonstrates forward sampling and likelihood-weighted sampling from a Bayesian network, comparing empirical marginals with exact inference results.
|
Command sampling demonstrates forward sampling and likelihood-weighted sampling from a Bayesian network, comparing empirical marginals with exact inference results. |
|
Command structure_learning demonstrates learning a Bayesian network structure from synthetic data using HillClimbSearch with BIC scoring.
|
Command structure_learning demonstrates learning a Bayesian network structure from synthetic data using HillClimbSearch with BIC scoring. |