HIGH-LEVEL PRIMITIVES FOR RECURSIVE MAXIMUM-LIKELIHOOD-ESTIMATION

This paper proposes a high-level language constituted of a small numbe r of primitives and macros for describing recursive maximum likelihood (ML) estimation algorithms, This language is applicable to estimation problems involving linear Gaussian models or processes taking values in a finite set, The use of high-level primitives allows the developme nt of highly modular ML estimation algorithms based on simple numerica l building blocks, The primitives, which correspond to the combination of different measurements, the extraction of sufficient statistics, a nd the conversion of the status of a variable from unknown to observed , or vice versa, are first defined for linear Gaussian relations speci fying mixed deterministic/stochastic information about the system vari ables, These primitives are used to define other macros and are illust rated by deriving new filtering and smoothing algorithms for linear de scriptor systems, The primitives are then extended to finite state pro cesses and used to implement the Viterbi ML state sequence estimator f or a hidden Markov model.