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# VEBPGibbsStrategy 

#### 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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1. VEBPGibbsStrategy
2. RaisingStrategy
3. SolvingStrategy
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### Instance Constructors

1. new VEBPGibbsStrategy(problem: Problem, raisingCriteria: RaisingCriteria, scoreThreshold: Double, determThreshold: Double, bpIters: Int, numSamples: Int, burnIn: Int, interval: Int, blockToSampler: BlockSamplerCreator)

### Value Members

1. final def !=(arg0: Any): Boolean
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2. final def ##(): Int
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3. final def ==(arg0: Any): Boolean
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4. final def asInstanceOf[T0]: T0
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5. val blockToSampler
6. val bpIters: Int
7. val burnIn: Int
8. def chainNonConstraintFactors[ParentValue, Value](chainComp: ChainComponent[ParentValue, Value]): List[Factor[Double]]

Get non-constraint factors associated with a single Chain component.

Get non-constraint 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
9. def clone(): AnyRef
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protected[java.lang]
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@throws( ... )
10. 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
11. val determThreshold: Double
12. 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
VEBPGibbsStrategySolvingStrategy
13. final def eq(arg0: AnyRef): Boolean
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14. def equals(arg0: Any): Boolean
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15. 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.

Definition Classes
SolvingStrategy
16. def finalize(): Unit
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@throws( classOf[java.lang.Throwable] )
17. final def getClass(): Class[_]
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18. def hasDeterminism(problem: Problem, v: Variable[_]): Boolean
19. def hashCode(): Int
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20. val interval: Int
21. final def isInstanceOf[T0]: Boolean
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22. final def ne(arg0: AnyRef): Boolean
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23. def nonConstraintFactors(): List[Factor[Double]]

Get all of the non-constraint factors needed for solving.

Get all of the non-constraint factors needed for solving. This includes subproblem factors.

returns

Non-constraint factors for solving.

Definition Classes
RaisingStrategySolvingStrategy
24. final def notify(): Unit
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25. final def notifyAll(): Unit
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26. val numSamples: Int
27. def recurse(subproblem: NestedProblem[_])

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
VEBPGibbsStrategyRaisingStrategy
28. val scoreThreshold: Double
29. def subproblemNonConstraintFactors[ParentValue, Value](chainComp: ChainComponent[ParentValue, Value]): Map[ParentValue, List[Factor[Double]]]

Get the non-constraint factors associated with all subproblems of a Chain component.

Get the non-constraint 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
30. final def synchronized[T0](arg0: ⇒ T0): T0
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31. def toString(): String
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32. final def wait(): Unit
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33. final def wait(arg0: Long, arg1: Int): Unit
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34. final def wait(arg0: Long): Unit
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