class VEBPGibbsStrategy extends RaisingStrategy
A solving strategy that chooses between VE, BP, and Gibbs based on a score of the elminiation order and determinism
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 VEBPGibbsStrategy
 RaisingStrategy
 SolvingStrategy
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 new VEBPGibbsStrategy(problem: Problem, raisingCriteria: RaisingCriteria, scoreThreshold: Double, determThreshold: Double, bpIters: Int, numSamples: Int, burnIn: Int, interval: Int, blockToSampler: BlockSamplerCreator)
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final
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 val blockToSampler: BlockSamplerCreator
 val bpIters: Int
 val burnIn: Int

def
chainNonConstraintFactors[ParentValue, Value](chainComp: ChainComponent[ParentValue, Value]): List[Factor[Double]]
Get nonconstraint factors associated with a single Chain component.
Get nonconstraint factors associated with a single Chain component.
 chainComp
Chain component to process.
 returns
All factors associated with the chain component that are needed for solving. This includes (possibly eliminated) subproblem factors.
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 RaisingStrategy

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def
constraintFactors(bounds: Bounds): List[Factor[Double]]
Get all of the constraint factors needed for solving.
Get all of the constraint factors needed for solving.
 bounds
Bounds for the returned constraint factors.
 returns
Constraint factors for solving.
 Definition Classes
 SolvingStrategy
 val determThreshold: Double

def
eliminate(toEliminate: Set[Variable[_]], toPreserve: Set[Variable[_]], factors: List[Factor[Double]]): (List[Factor[Double]], Map[Variable[_], Factor[_]])
Solve the problem by eliminating variables, leaving only the ones that belong in the solution.
Solve the problem by eliminating variables, leaving only the ones that belong in the solution.
 toEliminate
Variables to eliminate.
 toPreserve
Variables to preserve.
 factors
Factors over which to perform elimination.
 returns
A list of factors over the variables to preserve representing their joint distribution, and a map of recording factors for MPE.
 Definition Classes
 VEBPGibbsStrategy → SolvingStrategy

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eq(arg0: AnyRef): Boolean
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def
execute(bounds: Bounds = Lower): Unit
Solve the problem defined by all the components' current factors.
Solve the problem defined by all the components' current factors. This involves solving and incorporating subproblems as well. This will set the globals accordingly. All components in this problem and contained subproblems should be eliminated in the solution.
 bounds
Bounds for constraint factors. Defaults to
Lower
. This default is intended for the cases where it does not matter which bounds should be used because both upper and lower bounds would be the same.
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 SolvingStrategy

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getClass(): Class[_]
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 def hasDeterminism(problem: Problem, v: Variable[_]): Boolean

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 val interval: Int

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def
nonConstraintFactors(): List[Factor[Double]]
Get all of the nonconstraint factors needed for solving.
Get all of the nonconstraint factors needed for solving. This includes subproblem factors.
 returns
Nonconstraint factors for solving.
 Definition Classes
 RaisingStrategy → SolvingStrategy

final
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notify(): Unit
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final
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notifyAll(): Unit
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 val numSamples: Int

def
recurse(subproblem: NestedProblem[_]): VEBPGibbsStrategy
Returns a strategy that could be used to solve the nested problem.
Returns a strategy that could be used to solve the nested problem.
 subproblem
Unsolved nested problem to recurse on.
 returns
A strategy to solve the nested problem.
 Definition Classes
 VEBPGibbsStrategy → RaisingStrategy
 val scoreThreshold: Double

def
subproblemNonConstraintFactors[ParentValue, Value](chainComp: ChainComponent[ParentValue, Value]): Map[ParentValue, List[Factor[Double]]]
Get the nonconstraint factors associated with all subproblems of a Chain component.
Get the nonconstraint factors associated with all subproblems of a Chain component. This returns the existing solution if there is one. Otherwise, it chooses to solve or raise the subproblem based on the raising criteria.
 chainComp
Chain component whose subproblems are to be processed.
 returns
All factors associated with subproblems that are needed for solving, grouped by parent value.
 Definition Classes
 RaisingStrategy

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