In this study, the authors focus on estimating the unknown constant latency probability of non-linear systems with one-step randomly delayed measurements using maximum likelihood (ML) criterion. A new latency probabil...
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In this study, the authors focus on estimating the unknown constant latency probability of non-linear systems with one-step randomly delayed measurements using maximum likelihood (ML) criterion. A new latency probability estimation algorithm is proposed based on an expectation maximisation approach to obtain an approximate ML estimation of latency probability. The proposed algorithm consists of expectation step (E-step) and the maximisation step (M-step). In the E-step, the expectation of the complete data log-likelihood function is approximately computed based on the currently estimated latency probability, and in the M-step, the approximate expectation is maximised using the Newton approach. The efficacy of the proposed algorithm is illustrated in a numerical example concerning univariate non-stationary growth model.
Parameters of a finite mixture model are often estimated by the expectation-maximization (EM) algorithm where the observed datalog-likelihoodfunction is maximized. This paper proposes an alternative approach for fit...
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Parameters of a finite mixture model are often estimated by the expectation-maximization (EM) algorithm where the observed datalog-likelihoodfunction is maximized. This paper proposes an alternative approach for fitting finite mixture models. Our method, called the iterativeMonte Carlo classification (IMCC), is also an iterative fitting procedure. Within each iteration, it first estimates the membership probabilities for each data point, namely the conditional probability of a data point belonging to a particular mixing component given that the data point value is obtained, it then classifies each data point into a component distribution using the estimated conditional probabilities and the Monte Carlo method. It finally updates the parameters of each component distribution based on the classified data. Simulation studies were conducted to compare IMCC with some other algorithms for fittingmixture normal, and mixture t, densities.
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