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Crate cvx_bayes

Crate cvx_bayes 

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§cvx-bayes — Bayesian Network Inference for ChronosVector

Provides discrete Bayesian networks for probabilistic reasoning over temporal episode data. Designed to answer questions that vector similarity alone cannot:

  • P(success | task_type=clean, region=R5, action=navigate)
  • “How confident am I in this prediction?” (posterior variance)
  • “What’s the most likely action given partial observations?”

§Theoretical Foundation

A Bayesian network is a directed acyclic graph (DAG) where:

  • Nodes are random variables (task_type, region, action, success)
  • Edges encode conditional dependencies
  • CPTs (Conditional Probability Tables) store P(X | parents(X))

Inference computes the posterior P(query | evidence) by propagating beliefs through the graph. For small networks (< 20 variables), exact inference via variable elimination is tractable.

§References

  • Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems
  • Koller & Friedman (2009). Probabilistic Graphical Models
  • Murphy, K. (2012). Machine Learning: A Probabilistic Perspective

Re-exports§

pub use cpt::Cpt;
pub use network::BayesianNetwork;
pub use variable::Variable;
pub use variable::VariableId;

Modules§

cpt
Conditional Probability Tables (CPTs) for Bayesian network nodes.
network
Bayesian network: DAG of variables with CPTs and inference.
variable
Discrete random variables for Bayesian network nodes.