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BetheApproximation

Struct BetheApproximation 

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pub struct BetheApproximation {
    pub max_iterations: usize,
    pub tolerance: f64,
    pub damping: f64,
}
Expand description

Bethe approximation for structured variational inference.

Uses the factor graph structure to define a structured approximation. More accurate than mean-field but still tractable.

The Bethe free energy is: F_Bethe = Σ_α H(b_α) - Σ_i (d_i - 1) H(b_i) - Σ_α <log ψ_α>_b_α

where:

  • b_α are factor beliefs (cluster marginals)
  • b_i are variable beliefs (node marginals)
  • d_i is the degree of variable i
  • H is entropy

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§max_iterations: usize

Maximum iterations

§tolerance: f64

Convergence tolerance

§damping: f64

Damping factor for message updates

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impl BetheApproximation

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pub fn new(max_iterations: usize, tolerance: f64, damping: f64) -> Self

Create with custom parameters.

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pub fn run(&self, graph: &FactorGraph) -> Result<HashMap<String, ArrayD<f64>>>

Run Bethe approximation using belief propagation.

Returns variable beliefs (marginals).

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pub fn compute_free_energy( &self, graph: &FactorGraph, variable_beliefs: &HashMap<String, ArrayD<f64>>, factor_beliefs: &HashMap<String, ArrayD<f64>>, ) -> Result<f64>

Compute Bethe free energy.

F_Bethe = Σ_α E_bα[log bα - log ψα] - Σ_i (d_i - 1) H(b_i)

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pub fn compute_factor_beliefs( &self, graph: &FactorGraph, variable_beliefs: &HashMap<String, ArrayD<f64>>, ) -> Result<HashMap<String, ArrayD<f64>>>

Compute factor beliefs from variable beliefs and factor potentials.

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impl Default for BetheApproximation

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fn default() -> Self

Returns the “default value” for a type. Read more

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