/**
 * Viterbi decoding from observation likelihoods.
 *
 * @param {ArrayLike<ArrayLike<number>>} prob - P[obs(t) | state=s], indexed
 *   [state][frame]; non-negative.
 * @param {ArrayLike<ArrayLike<number>>} transition - Row-stochastic transition
 *   matrix [n_states x n_states].
 * @param {ArrayLike<number>|null} [p_init=null] - Initial state distribution;
 *   uniform when null.
 * @param {boolean} [return_logp=false] - Also return the log-probability of the
 *   decoded path.
 * @returns {number[]|{states: number[], logp: number}}
 */
export function viterbi(prob: ArrayLike<ArrayLike<number>>, transition: ArrayLike<ArrayLike<number>>, p_init?: ArrayLike<number> | null, return_logp?: boolean): number[] | {
    states: number[];
    logp: number;
};
/**
 * Viterbi decoding from discriminative (mutually exclusive) state posteriors.
 *
 * Observation likelihood ∝ P(state | obs) / P(state); computed in
 * log space as `log(prob) - log(p_state)`. This function forms the ratio and
 * defers the log/decoding to `viterbi`.
 *
 * @param {ArrayLike<ArrayLike<number>>} prob - P[state=s | obs(t)], indexed
 *   [state][frame]; each frame (column) should sum to 1.
 * @param {ArrayLike<ArrayLike<number>>} transition - Row-stochastic transition
 *   matrix [n_states x n_states].
 * @param {ArrayLike<number>|null} [p_state=null] - Marginal state distribution;
 *   uniform when null.
 * @param {ArrayLike<number>|null} [p_init=null] - Initial state distribution;
 *   uniform when null.
 * @param {boolean} [return_logp=false] - Also return the (unnormalized)
 *   log-probability of the decoded path.
 * @returns {number[]|{states: number[], logp: number}}
 */
export function viterbi_discriminative(prob: ArrayLike<ArrayLike<number>>, transition: ArrayLike<ArrayLike<number>>, p_state?: ArrayLike<number> | null, p_init?: ArrayLike<number> | null, return_logp?: boolean): number[] | {
    states: number[];
    logp: number;
};