Nonuniform Illumination Correction Algorithm for Underwater Images Using Maximum Likelihood Estimation Method

Bearing Fault Detection Based on Maximum Likelihood Estimation and Optimized ANN Using the Bees Algorithm

Rotating machinery is the most common machinery in industry. The root of the faults in rotating machinery is often faulty rolling element bearings. This paper presents a technique using optimized artificial neural network by the Bees Algorithm for automated diagnosis of localized faults in rolling element bearings. The inputs of this technique are a number of features (maximum likelihood estima...

Approximate Maximum Likelihood Method for Frequency Estimation

A frequency can be estimated by few Discrete Fourier Transform (DFT) coefficients, see Rife and Vincent (1970), Quinn (1994, 1997). This approach is computationally efficient. However, the statistical efficiency of the estimator depends on the location of the frequency. In this paper, we explain this approach from a point of view of an Approximate Maximum Likelihood (AML) method. Then we enhanc...

An Algorithm for Maximum Likelihood Estimation Using an Efficient Method for Approximating Sensitivities

Maximum Log Likelihood Estimation using EM Algorithm and Partition Maximum Log Likelihood Estimation for Mixtures of Generalized Lambda Distributions

Maximum likelihood estimation from fuzzy data using the EM algorithm

A method is proposed for estimating the parameters in a parametric statistical model when the observations are fuzzy and are assumed to be related to underlying crisp realizations of a random sample. This method is based on maximizing the observeddata likelihood defined as the probability of the fuzzy data. It is shown that the EM algorithm may be used for that purpose, which makes it possible ...