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Maximum Likelihood & Gauss-Markov Parameter Estimation

Drayton D. Boozer, Ph.D, PE

The need to fit mathematical models to measured data arises often in science and engineering. Parameter estimation is a disciple that provides estimates of unknown parameters in a system or process model based on measured data. The professional analyst can use the model that results from the application of parameter estimation to explain measured data to customers in a concise, compelling way.

The course develops maximum likelihood and Gauss-Markov parameter estimators for a linear measurement model. Six basic assumptions about measurement errors that were introduced in Course G429 are used to clearly explain when to apply each estimation method. Confidence limits for the estimated parameters are developed. Finally, maximum likelihood and Gauss-Markov estimators are compared to the least squares estimator developed in Course G429.

A detailed thermal conductivity example problem is solved and discussed. The quiz is designed to enhance understanding of the course material.

This is the second in a series of courses planned to give a working level understanding of the field of parameter estimation to practicing engineers, land surveyors, and architects.

This course includes a multiple-choice quiz at the end, which is designed to enhance the understanding of the course materials.

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NY PE & PLS: You must choose courses that are technical in nature or related to matters of laws and ethics contributing to the health and welfare of the public. NY Board does not accept courses related to office management, risk management, leadership, marketing, accounting, financial planning, real estate, and basic CAD. Specific course topics that are on the borderline and are not acceptable by the NY Board have been noted under the course description on our website.