/** Options for the multivariate samplers (shared shape with mvnSample). */
export interface SampleSeedOptions {
    /** Seed for the deterministic RNG (reproducible draws). Omit for a
     * time-seeded, non-reproducible generator. */
    seed?: string | number;
}
/**
 * Draw `n` samples from a Dirichlet(`alpha`) distribution via the
 * Gamma-normalization method. Each sample is a length-k vector on the simplex
 * (non-negative, summing to 1). Returns an `n`-length array of length-k vectors.
 *
 * @example
 * dirichletSample([2, 3, 5], 10000, { seed: 42 });
 * // empirical mean ≈ [0.2, 0.3, 0.5] = αᵢ / Σα
 */
export declare function dirichletSample(alpha: readonly number[], n?: number, opts?: SampleSeedOptions): number[][];
/**
 * Dirichlet(`alpha`) probability density at a point `x` on the simplex.
 * `pdf(x) = (1 / B(α)) · Πᵢ xᵢ^(αᵢ−1)` with
 * `B(α) = Πᵢ Γ(αᵢ) / Γ(Σαᵢ)`. Matches `scipy.stats.dirichlet.pdf`.
 *
 * @example
 * dirichletPdf([0.2, 0.3, 0.5], [2, 3, 4]); // 7.56
 */
export declare function dirichletPdf(x: readonly number[], alpha: readonly number[]): number;
/**
 * Draw `n` samples from a Wishart(`df`, `scale`) distribution via the Bartlett
 * decomposition: with `L = chol(scale)` and a lower-triangular `A` whose
 * diagonal entries are `√χ²(df−i)` (0-indexed) and whose strictly-lower entries
 * are N(0,1), each sample is `W = (L·A)(L·A)ᵀ` — a `p×p` symmetric
 * positive-definite matrix. Returns an `n`-length array of `p×p` matrices.
 *
 * `df` must exceed `p − 1` (so every χ² degree-of-freedom is positive). The
 * mean of the distribution is `df · scale`.
 *
 * @example
 * wishartSample(6, [[2, 0.5], [0.5, 1]], 10000, { seed: 7 });
 * // empirical mean ≈ [[12, 3], [3, 6]] = df · scale
 */
export declare function wishartSample(df: number, scale: readonly (readonly number[])[], n?: number, opts?: SampleSeedOptions): number[][][];
//# sourceMappingURL=multivariate-sampling.d.ts.map