package decision

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  1. class AnytimeDecisionMetropolisHastings [T, U] extends DecisionMetropolisHastings[T, U] with UnweightedSampler with AnytimeProbQuerySampler

    Anytime Decision Metropolis-Hastings sampler.

  2. trait DecisionAlgorithm [T, U] extends Algorithm

    Trait that defines some common interface functions for decision algorithms.

    Trait that defines some common interface functions for decision algorithms. Every decision algorithm must define the function computeUtility().

  3. abstract class DecisionImportance [T, U] extends Importance with DecisionAlgorithm[T, U]

    Importance sampling for decisions.

    Importance sampling for decisions. Almost the exact same as normal importance sampling except that it keeps track of utilities and probabilities (to compute expected utility) and it implements DecisionAlgorithm trait.

  4. abstract class DecisionMetropolisHastings [T, U] extends MetropolisHastings with DecisionAlgorithm[T, U]

    Metropolis-Hastings Decision sampler.

    Metropolis-Hastings Decision sampler. Almost the exact same as normal MH except that it keeps track of utilities and probabilities (to compute expected utility) and it implements DecisionAlgorithm trait

  5. trait DecisionPolicy [T, U] extends AnyRef

    Abstract base class for all Decision Policies.

    Abstract base class for all Decision Policies. Must define two functions: toFcn: T => Element[U] - this is the function that is called to compute the decision for a parent value.

    toUtility: T => Element[Double] - this returns the expected utility of the decision for a parent value. Used in backward induction algorithm.

    T

    The parent value type

    U

    The decision type

  6. class DecisionPolicyExact [T, U] extends DecisionPolicy[T, U]

    An exact decision policy.

    An exact decision policy. This policy is exact because every possible value of the parent must have a defined policy. This makes it suitable for variable elimination algorithms or sampling algorithms if the range of the parent is small and enough samples are generated.

  7. class DecisionPolicyNN [T, U] extends DecisionPolicy[T, U]

    A nearest neighbor decision policy.

    A nearest neighbor decision policy. This policy computes an approximate decision from a sampling algorithm. The input to the class is an index (which holds (parent, decision) samples) a function that will combine a set of (decision, utility) samples into a single decision, and numNNSamples, the number of samples to use in a nearest neighbor algorithm. By default, this uses a VP-tree to store the samples.

  8. abstract class MultiDecisionAlgorithm extends OneTime

    Abstract class common to all multi-decision algorithms.

    Abstract class common to all multi-decision algorithms. Multi-decision algorithms implement backward induction by 1) determining the order in which decisions can be computed 2) Implementing a single decision algorithm on each decision (in the proper order).

    Note: Only OneTime algorithms are supported in multi-decision algorithms.

  9. class MultiDecisionVariableElimination extends MultiDecisionAlgorithm

    A multi-decision algorithm that uses Variable Elimination for each decision.

  10. class OneTimeDecisionMetropolisHastings [T, U] extends DecisionMetropolisHastings[T, U] with UnweightedSampler with OneTimeProbQuerySampler

    One-time Decision Metropolis-Hastings sampler.

  11. class OneTimeMultiDecisionImportance extends MultiDecisionAlgorithm

    A OneTime multi-decision algorithm that uses Importance sampling for each decision.

  12. class OneTimeMultiDecisionMetropolisHastings extends MultiDecisionAlgorithm

    A OneTime multi-decision algorithm that uses Metropolis-Hastings sampling for each decision.

    A OneTime multi-decision algorithm that uses Metropolis-Hastings sampling for each decision. A user must supple an instance of a ProposalMakerType, which indicates how to create a proposal scheme for each decision.

  13. trait OneTimeProbQueryDecision [T, U] extends OneTimeProbQuery with DecisionAlgorithm[T, U]

    Trait for one time Decision Algorithms.

  14. class ProbQueryVariableEliminationDecision [T, U] extends OneTimeProbQuery with ProbabilisticVariableEliminationDecision with DecisionAlgorithm[T, U]

    Decision VariableElimination algorithm that computes the expected utility of decision elements using the default elimination order.

  15. trait ProbabilisticVariableEliminationDecision extends VariableElimination[(Double, Double)]

    Trait for Decision based Variable Elimination.

    Trait for Decision based Variable Elimination. This implementation is hardcoded to use. Double utilities.

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