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[PENDING] Maximum likelihood estimation from fuzzy data using the EM algorithm

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Figure 1: Ten trapezoidal fuzzy numbers considered in Example 2. The heights have been jittered to better separate the membership functions.
Figure 2: Contour plot of the observed data log likelihood function and trajectory in parameter space (broken line) from the initial parameter values (m (0) , σ (0) ) (o) to the final MLE ( m, b b σ) (x), for the data of Example 2.
Figure 3: Fuzzy information system used to encode the simulated fuzzy data of Ex- Ex-ample 3.
Figure 4: Mean plus or minus one standard deviation (over 100 trials) of m b for a fuzzy sample of size n from a standard normal distribution, as a function of n (Example 3).
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