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Furthermore, it is often of interest to update the random field model in light of available or assumed observations on the random field or related variables.
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The ideal cofactor matrix ("criterion" matrix) can be computed from the Taylor-Karman structure, and the cofactor matrix of the estimated point coordinates are obtained from the design matrix of the assumed observation model (distance measurements in this study).
Currently the most common estimation methods assume observations are sampled using a probabilistic sampling scheme, however this assumption is often not met.
Hence, we also refer to them as mandatory fusion models because they assume observations correspond.
We assume observations are realizations of independent draws from their respective probability models.
However, these procedures assume observations are independent and may not be suitable for CRTs since they do not take into account the intra-cluster correlation.
The received intensities with the LIDAR were estimated to be consistent with simple calculations assuming observation geometry, and the receiving system was confirmed to be sound.
Because the RSSI and CSI-norm methods assume observation data from a single antenna, the RSSI and CSI-norm averaged across the antennas in the array are respectively used as the observation data for the two methods in this paper.
A marginal Poisson distribution of the dependent variable and correlation between observations during one summer/winter were assumed, while observations from different summer/winter periods were considered independent.
We assumed that observations in this interval are yeast cell size observations.
This paper considers discrete survey sampling in an area, when it can be assumed that observations near each other will be positively correlated.
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