Class for handling sampling on a block
Caches factors according to assignments of values in the Markov blanket, which avoids recomputing the same factors repeatedly Takes advantage of the fact in Gibbs sampling, nearby samples tend to be highly correlated
extends BaseUnweightedSampler with Gibbs[Double]
The default trait for creating blocks.
Specialized sampling for continuous (i.e.
A VE-like procedure that works well on large but highly sparse blocks.
extends SimpleBlockSampler with DoubleWeight
Assigns weights to continuous variables based on a Gaussian PDF with a static variance
- trait Gibbs [T] extends BaseUnweightedSampler with FactoredAlgorithm[T]
- abstract class ProbQueryGibbs extends BaseUnweightedSampler with ProbabilisticGibbs with UnweightedSampler
- trait ProbabilisticGibbs extends BaseUnweightedSampler with Gibbs[Double]
- class SimpleBlockSampler extends BlockSampler
- class StateNotFoundException extends RuntimeException