Maximum likelihood estimators and worst case optimal algorithms for system identification

R. Tempo, G. W. Wasilkowski

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


For system identification problems with stochastic noise, maximum likelihood estimators are frequently used. If noise is deterministic, worst case optimal algorithms should be considered. In this paper we study the following problem: Under what circumstances are maximum likelihood estimators optimal in the worst case?

Original languageEnglish
Pages (from-to)265-270
Number of pages6
JournalSystems and Control Letters
Issue number4
StatePublished - Apr 1988

Bibliographical note

Funding Information:
* This research was developed while Dr. Tempo was visiting the Department of Computer Science, Columbia University supported by a NATO-CNR advanced fellowship and by funds of Ministero della Pubblica Istruzione. ** Supported in part by the National Science Foundation under Grant DCR-86-03674.


  • Maximum likelihood estimators
  • Noisy information
  • Optimal algorithms
  • System identification
  • Worst case

ASJC Scopus subject areas

  • Control and Systems Engineering
  • General Computer Science
  • Mechanical Engineering
  • Electrical and Electronic Engineering


Dive into the research topics of 'Maximum likelihood estimators and worst case optimal algorithms for system identification'. Together they form a unique fingerprint.

Cite this