FactorisedGaussian
@Serializable
@SerialName(value = "FactorisedGaussian")
Per-coordinate Gaussian: each w_i ~ N(weights[i], exploration / precision[i]). Cheap O(n) draws; ignores cross-feature correlations.
Functions
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open override fun evaluate(snapshot: DiagonalRegressionResult, x: VectorView, rng: Random, exploration: Double = 1.0): Double
Sum of independent normals: predict(x) + sqrt(exploration * Sum x_i^2 / precision[i]) * N(0,1).
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open override fun sample(snapshot: DiagonalRegressionResult, rng: Random, exploration: Double = 1.0): VectorView
Draw a weight vector from the posterior at exploration variance scale. exploration = 0.0 collapses to the point estimate; 1.0 is the calibrated posterior.
evaluate
open override fun evaluate(snapshot: DiagonalRegressionResult, x: VectorView, rng: Random, exploration: Double = 1.0): Double(source)
Sum of independent normals: predict(x) + sqrt(exploration * Sum x_i^2 / precision[i]) * N(0,1).
sample
open override fun sample(snapshot: DiagonalRegressionResult, rng: Random, exploration: Double = 1.0): VectorView(source)
Draw a weight vector from the posterior at exploration variance scale. exploration = 0.0 collapses to the point estimate; 1.0 is the calibrated posterior.