Interface: DynamicDiscoveryRequest

@kortexya/reasoninglayer

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@kortexya/reasoninglayer / DynamicDiscoveryRequest

Interface: DynamicDiscoveryRequest

Defined in: src/types/statistical.ts:428

Request for dynamic causal discovery with configurable strategy.

Remarks

Supports constraint-based (PC), Bayesian (MCMC), and hybrid strategies for learning causal structure from observational data.

Properties

alpha?

optional alpha?: number | null

Defined in: src/types/statistical.ts:430

Significance level for independence testing (default: 0.05).

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edgeThreshold?

optional edgeThreshold?: number | null

Defined in: src/types/statistical.ts:432

Hybrid-specific: edge confidence threshold (default: 0.5).

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mcmcBurnIn?

optional mcmcBurnIn?: number | null

Defined in: src/types/statistical.ts:434

MCMC-specific: burn-in period (default: 100).

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mcmcSamples?

optional mcmcSamples?: number | null

Defined in: src/types/statistical.ts:436

MCMC-specific: number of samples (default: 500).

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minSamples?

optional minSamples?: number | null

Defined in: src/types/statistical.ts:438

Minimum samples before discovery (default: 30).

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strategy?

optional strategy?: DiscoveryStrategy | null

Defined in: src/types/statistical.ts:440

Structure learning strategy (default: “pc”).

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temporalTiers?

optional temporalTiers?: string[][] | null

Defined in: src/types/statistical.ts:446

Temporal tiers for background knowledge (tPC algorithm). Each tier is a list of variables that belong to that temporal tier. Tiers are ordered from past to future (tier 0 = earliest).

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useActiveLearning?

optional useActiveLearning?: boolean | null

Defined in: src/types/statistical.ts:448

Enable active learning recommendations (default: true).

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useGes?

optional useGes?: boolean | null

Defined in: src/types/statistical.ts:450

Enable GES refinement (default: true).